Question: Categorization of sensor inaccuracies for the statistical modeling of environmental awareness in autonomous driving systems.

This paper explores the intricacies of simulation in evolutionary robotics, specifically focusing on the impact of noise and the "reality gap." It is well-known that naive simulations can lead to significant discrepancies between simulated and real-world robotic performance. To address this, the authors advocate for the use of carefully calibrated simulations that accurately incorporate noise. The study presents findings from a series of experiments designed to evaluate the effectiveness of this approach. A simulation was developed for a two-wheeled Khepera robot equipped with infrared and ambient light sensors. The simulation included a detailed, empirically validated model of the robot-environment dynamics. Artificial evolution techniques were employed to generate recurrent dynamical network controllers for the robot to perform tasks such as obstacle avoidance and light seeking. The controllers were evolved under varying levels of simulated noise. To assess the simulation's accuracy, the evolved controllers were then implemented on the actual robot, and the similarity of the robot's behavior in simulation versus reality was analyzed. The results showed that the level of noise in the simulation significantly influenced the correspondence between simulated and real-world behavior. When realistic noise levels were applied, the controllers developed in simulation produced behaviors that were almost identical to those observed in reality. This study provides empirical evidence that realistic simulations, when properly designed to include noise, can effectively generate robot controllers that perform consistently across both simulated and real-world environments, particularly for specific types of robot-environment interactions.

This article addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. Unlike the case with well-calibrated and well-tested infrastructure sensors, humans are less reliable, and the likelihood that participants' measurements are correct is often unknown a priori. Given a set of human participants of unknown trustworthiness together with their sensory measurements, we pose the question of whether one can use this information alone to determine, in an analytically founded manner, the probability that a given measurement is true. In our previous conference paper, we offered the first maximum likelihood solution to the aforesaid truth discovery problem for corroborating observations only. In contrast, this article extends the conference paper and provides the first maximum likelihood solution to handle the cases where measurements from different participants may be conflicting. The article focuses on binary measurements. The approach is shown to outperform our previous work used for corroborating observations, the state-of-the-art fact-finding baselines, as well as simple heuristics such as majority voting.

User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.

A redundant inertial measurement unit (IMU) is an inertial sensing device composed by more than three accelerometers and three gyroscopes. This paper analyses the performance of redundant IMUs and their potential benefits and applications in airborne remote sensing and photogrammetry. The theory of redundant IMUs is presented through two different algorithmic approaches. The first approach is to combine the inertial observations, in the observation space, to generate a “synthetic” non-redundant IMU. The second approach modifies the INS mechanization equations so that they directly account for the observational redundancy. The paper ends with an empirical assesment of the concept. For this purpose, redundant IMU data was generated by combining two IMUs in a non-orthogonal configuration and flying them. Preliminary results of this flight are presented.

Detection of traversable road regions, positive and negative obstacles, and water hazards is a fundamental task for autonomous driving vehicles, especially in off-road environment. This paper proposes an efficient method, called Lidar-histogram. It can be used to integrate the detection of traversable road regions, obstacles and water hazards into one single framework. The weak assumption of the Lidar-histogram is that a decent-sized area in front of the vehicle is flat. The Lidar-histogram is derived from an efficient organized map of Lidar point cloud, called Lidar-imagery, to index, describe and store Lidar data. The organized point-cloud map can be easily obtained by indexing the original unordered 3D point cloud to a Lidar-specific 2D coordinate system. In the Lidar-histogram representation, the 3D traversable road plane in front of vehicle can be projected as a straight line segment, and the positive and negative obstacles are projected above and below the line segment, respectively. In this way, the problem of detecting traversable road and obstacles is converted into a simple linear classification task in 2D space. Experiments have been conducted in different kinds of off-road and urban scenes, and we have obtained very promising results.

The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.

In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.

Reliable autonomous navigation is an active research topic that has drawn the attention for decades, however, human factors such as navigational comfort has not received the same level of attention. This work proposes the concept of “comfortable map” and presents a navigation approach for autonomous passenger vehicles which in top of being safe and reliable is comfortable. In our approach we first extract information from users preference related to comfort while sitting on a robotic wheelchair under different conditions in an indoor corridor environment. Human-comfort factors are integrated to a geometric map generated by SLAM framework. Then a global planner computes a safe and comfortable path which is followed by the robotic wheelchair. Finally, an evaluation with 29 participants using a fully autonomous robotic wheelchair, showed that more than 90% of them found the proposed approach more comfortable than a shortest-path state of the art approach.

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.


Question: Categorization of sensor mistakes for the statistical emulation of environmental awareness in autonomous driving systems.

This paper recounts the "Making Bertha Drive" project, an autonomous recreation of the pioneering journey taken by Bertha Benz 125 years earlier, from Mannheim to Pforzheim in Germany. The Mercedes-Benz S-Class S 500 INTELLIGENT DRIVE undertook this historic route autonomously, using near-production sensor technology and relying exclusively on vision and radar sensors, complemented by precise digital maps, to interpret and navigate complex traffic conditions. The Bertha Benz Memorial Route presented a significant challenge for autonomous driving due to its diverse and demanding environments, including rural roads, 23 small villages, and bustling city centers like Mannheim and Heidelberg. The 103 km journey encompassed a wide range of traffic scenarios such as intersections with and without traffic lights, roundabouts, and narrow sections with oncoming traffic. This paper offers an in-depth look at the autonomous vehicle's capabilities, detailing its vision and radar-based perception systems, the use of digital road maps, video-based self-localization, and sophisticated motion planning strategies for navigating intricate urban settings.

The recent DARPA-sponsored automated vehicle "Challenges" have generated strong interest in both the research community and the general public, raising consciousness about the possibilities for vehicle automation. Driverless vehicles make good subjects for the visually-oriented media, and they pose enough interesting research challenges to occupy generations of graduate students. However, automated vehicles also have the potential to help solve a variety of real-world problems. Engineers need to think carefully about which of those problems we are actually solving.A well-engineered system should be designed to satisfy specific needs, and those needs should be reflected in the definition of system requirements. Alternative technological approaches can then be evaluated and traded off based on their ability to meet the requirements. The article describes the rather different needs of the public road transportation system and the military transportation system, and then shows how those needs influence the requirements for automated vehicle systems. These requirements point toward significantly different technical approaches, but it is possible to find some limited areas of technical commonality.

Road vehicle travel at a reasonable speed involves some risk, even when using computer-controlled driving with failure-free hardware and perfect sensing. A fully-automated vehicle must continuously decide how to allocate this risk without a human driver’s oversight. These are ethical decisions, particularly in instances where an automated vehicle cannot avoid crashing. In this chapter, I introduce the concept of moral behavior for an automated vehicle, argue the need for research in this area through responses to anticipated critiques, and discuss relevant applications from machine ethics and moral modeling research. 1 Ethical Decision Making for Automated Vehicles Vehicle automation has progressed rapidly this millennium, mirroring improvements in machine learning, sensing, and processing. Media coverage often focuses on the anticipated safety benefits from automation, as computers are expected to be more attentive, precise, and predictable than human drivers. Mentioned less often are the novel problems from automated vehicle crash. The first problem is liability, as it is currently unclear who would be at fault if a vehicle crashed while self-driving. The second problem is the ability of an automated vehicle to make ethically-complex decisions when driving, particularly prior to a crash. This chapter focuses on the second problem, and the application of machine ethics to vehicle automation. Driving at any significant speed can never be completely safe. A loaded tractor trailer at 100 km/hr requires eight seconds to come to a complete stop, and a passenger car requires three seconds [1]. Truly safe travel requires accurate predictions of other vehicle behavior over this time frame, something that is simply not possible given the close proximities of road vehicles. To ensure its own safety, an automated vehicle must continually assess risk: the risk of traveling a certain speed on a certain curve, of crossing the centerline to

This paper describes a method for detecting and recognizing road signs in gray-level and color images acquired by a single camera mounted on a moving vehicle. The method works in three stages. First, the search for the road sign is reduced to a suitable region of the image by using some a priori knowledge on the scene or color clues (when available). Secondly, a geometrical analysis of the edges extracted from the image is carried out, which generates candidates to be circular and triangular signs. Thirdly, a recognition stage tests by cross-correlation techniques each candidate which, if validated, is classi ed according to the data-base of signs. An extensive experimentation has shown that the method is robust against low-level noise corrupting edge detection and contour following, and works for images of cluttered urban streets as well as country roads and highways. A further improvement on the detection and recognition scheme has been obtained by means of temporal integration based on Kalman ltering methods of the extracted information. The proposed approach can be very helpful for the development of a system for driving assistance.

In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.

With the rapid progress in the development of wireless technology, Global Positioning System (GPS) based vehicle navigation systems are being widely deployed in automobiles to serve the location-based needs of users and for efficient traffic management. An essential process in vehicle navigation is to map match the position obtained from GPS (or/and other sensors) on a road network map. This process of map matching in turn helps in mitigating errors from navigation solution. GPS based vehicle navigation systems have difficulties in tracking vehicles in urban canyons due to poor satellite availability. High Sensitivity GPS (HS GPS) receivers can alleviate this problem by acquiring and tracking weak signals (and increasing the availability), but at the cost of high measurement noise and errors due to multipath and cross correlation. Position and velocity results in such conditions are typically biased and have unknown distributions. Thus filtering and other statistical methods are difficult to implement. Soft computing has replaced classical computing on many fronts where uncertainties are difficult to model. Fuzzy logic, based on fuzzy reasoning concepts, is one of the most widely used soft computational methods. In many circumstances, it can take noisy, imprecise input, to yield crisp (i.e. numerically accurate) output. Fuzzy logic can be applied effectively to map match the output from a HS GPS receiver in urban canyons because of its inherent tolerance to imprecise inputs. This paper describes a map matching algorithm based on fuzzy logic. The input of the system comes from a SiRF HS XTrac GPS receiver and a low cost gyro (Murata ENV-05G). The results show an improvement in tracking the vehicle in urban canyon conditions.

In sensing applications where multiple sensors observe the same scene, fusing sensor outputs can provide improved results. However, if some of the sensors are providing lower quality outputs, e.g. when one or more sensors has a poor signal-tonoise ratio (SNR) and therefore provides very noisy data, the fused results can be degraded. In this work, a multi-sensor conflict measure is proposed which estimates multi-sensor conflict by representing each sensor output as interval-valued information and examines the sensor output overlaps on all possible n-tuple sensor combinations. The conflict is based on the sizes of the intervals and how many sensors output values lie in these intervals. In this work, conflict is defined in terms of how little the output from multiple sensors overlap. That is, high degrees of overlap mean low sensor conflict, while low degrees of overlap mean high conflict. This work is a preliminary step towards a robust conflict and sensor fusion framework. In addition, a sensor fusion algorithm is proposed based on a weighted sum of sensor outputs, where the weights for each sensor diminish as the conflict measure increases. The proposed methods can be utilized to (1) assess a measure of multi-sensor conflict, and (2) improve sensor output fusion by lessening weighting for sensors with high conflict. Using this measure, a simulated example is given to explain the mechanics of calculating the conflict measure, and stereo camera 3D outputs are analyzed and fused. In the stereo camera case, the sensor output is corrupted by additive impulse noise, DC offset, and Gaussian noise. Impulse noise is common in sensors due to intermittent interference, a DC offset a sensor bias or registration error, and Gaussian noise represents a sensor output with low SNR. The results show that sensor output fusion based on the conflict measure shows improved accuracy over a simple averaging fusion strategy.

As drivers back out of the driving task, when transported automatically by an intelligent car for a longer time, they are not always able to react properly, if a driver take over request occurs. This paper presents two ways, how to deal with this problem within the scope of a functional safety concept. Thereto, the difference between fully automatic and autonomous driving assistance systems is explained. Afterwards two different strategies to reach a safe state in consequence of a system boundary crossing are proposed. In the first case the fall back state is reached by a driver take over, in the second case by an automatic, active fail-safe mechanism. Subsequently the necessary components for monitoring and reaching a safe state and their embedment in a basic, functional architecture of a driving assistance system are described. In this context, special regard is paid to aspects of redundancy as well. In the end it is concluded, that the safety concept proposed here is crucial for guaranteeing enduring safety in an automatically driving car and in consequence for making automatic driving functions commercially ready for serial production.

Active safety systems are based upon the accurate and fast estimation of the value of important dynamical variables such as forces, load transfer, actual tire-road friction (kinetic friction) muk, and maximum tire-road friction available (potential friction) mup. Measuring these parameters directly from tires offers the potential for improving significantly the performance of active safety systems. We present a distributed architecture for a data-acquisition system that is based on a number of complex intelligent sensors inside the tire that form a wireless sensor network with coordination nodes placed on the body of the car. The design of this system has been extremely challenging due to the very limited available energy combined with strict application requirements for data rate, delay, size, weight, and reliability in a highly dynamical environment. Moreover, it required expertise in multiple engineering disciplines, including control-system design, signal processing, integrated-circuit design, communications, real-time software design, antenna design, energy scavenging, and system assembly.

Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return.In this paper, we propose VeTrack, a smartphone-only system that tracks the vehicle's location in real time using the phone's inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel "shadow" tracing method to accurately estimate the vehicle's trajectories despite arbitrary phone/vehicle poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle's real time location with almost negligible latency, with error of 2-4 parking spaces at 80-percentile.

This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.


Question: Factored language models and the implementation of generalized parallel backoff.

Over the past two decades, the field of statistical language modeling has seen significant advancements, aimed at enhancing the estimation of natural language phenomena distributions for applications in speech recognition and language technologies. Since the inception of the seminal model in 1980, numerous efforts have been dedicated to refining and surpassing existing benchmarks. This review not only chronicles these efforts but also identifies potential avenues for future exploration. We advocate for a Bayesian framework as a promising method for harmonizing linguistic theories with empirical data, thereby facilitating a more nuanced and comprehensive understanding of language. This approach holds the potential to integrate diverse linguistic insights with statistical data in a principled manner, paving the way for more sophisticated and accurate language models.

Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.

In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference. Our model is a nested hierarchical Pitman-Yor language model, where Pitman-Yor spelling model is embedded in the word model. We confirmed that it significantly outperforms previous reported results in both phonetic transcripts and standard datasets for Chinese and Japanese word segmentation. Our model is also considered as a way to construct an accurate word n-gram language model directly from characters of arbitrary language, without any “word” indications.

In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389

In this paper, we introduce a Web-scale linguistics search engine, Linggle, that retrieves lexical bundles in response to a given query. The query might contain keywords, wildcards, wild parts of speech (PoS), synonyms, and additional regular expression (RE) operators. In our approach, we incorporate inverted file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword retrieval commands. Word chunks are retrieved with counts, further filtering the chunks with the query as a RE, and finally displaying the results according to the counts, similarities, and topics. Clusters of synonyms or conceptually related words are also provided. In addition, Linggle provides example sentences from The New York Times on demand. The current implementation of Linggle is the most functionally comprehensive, and is in principle language and dataset independent. We plan to extend Linggle to provide fast and convenient access to a wealth of linguistic information embodied in Web scale datasets including Google Web 1T and Google Books Ngram for many major languages in the world.

The openly available biomedical literature contains over 5 billion words in publication abstracts and full texts. Recent advances in unsupervised language processing methods have made it possible to make use of such large unannotated corpora for building statistical language models and inducing high quality vector space representations, which are, in turn, of utility in many tasks such as text classification, named entity recognition and query expansion. In this study, we introduce the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics. We discuss the opportunities created by these resources and demonstrate their application. All resources introduced in this study are available under open licenses at http://bio.nlplab.org.

Training neural machine translation models is notoriously slow and requires abundant parallel corpora and computational power. In this work we propose an approach of transferring knowledge from separately trained language models to translation systems, also we investigate several techniques to improve translation quality when there is a lack of parallel data and computational resources. Our method is based on fusion between translation system and language model, and initialization of translation system with weights from pretrained language models. We show that this technique gives +2.2 BLEU score on En–Fr pair of WMT europarl-7 dataset and allows us to reach 20.7 BLEU on 20k parallel sentences in less than 8 hours of training on a single NVIDIA GeForce GTX 1080 Ti. We specifically note, that for this advance we use nothing but monolingual corpora for source and target languages. Significant part of presented results was obtained during DeepHack.Babel hackathon on low-resource machine translation organized by iPavlov Lab.

This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).

SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and experimentation with statistical language models for speech recognition and other applications. SRILM is freely available for noncommercial purposes. The toolkit supports creation and evaluation of a variety of language model types based on N-gram statistics, as well as several related tasks, such as statistical tagging and manipulation of N-best lists and word lattices. This paper summarizes the functionality of the toolkit and discusses its design and implementation, highlighting ease of rapid prototyping, reusability, and combinability of tools.

We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance.

This article presents a pragmatic approach to Chinese word segmentation. It differs from most previous approaches mainly in three respects. First, while theoretical linguists have defined Chinese words using various linguistic criteria, Chinese words in this study are defined pragmatically as segmentation units whose definition depends on how they are used and processed in realistic computer applications. Second, we propose a pragmatic mathematical framework in which segmenting known words and detecting unknown words of different types (i.e., morphologically derived words, factoids, named entities, and other unlisted words) can be performed simultaneously in a unified way. These tasks are usually conducted separately in other systems. Finally, we do not assume the existence of a universal word segmentation standard that is application-independent. Instead, we argue for the necessity of multiple segmentation standards due to the pragmatic fact that different natural language processing applications might require different granularities of Chinese words. These pragmatic approaches have been implemented in an adaptive Chinese word segmenter, called MSRSeg, which will be described in detail. It consists of two components: (1) a generic segmenter that is based on the framework of linear mixture models and provides a unified approach to the five fundamental features of word-level Chinese language processing: lexicon word processing, morphological analysis, factoid detection, named entity recognition, and new word identification; and (2) a set of output adaptors for adapting the output of (1) to different application-specific standards. Evaluation on five test sets with different standards shows that the adaptive system achieves state-of-the-art performance on all the test sets.


Question: Factored language models and the implementation of generalized parallel backoff.

Introducing SRILM: A Comprehensive Language Modeling Toolkit
SRILM, an extensive suite of C++ libraries, programs, and auxiliary scripts, is designed to facilitate the production and experimentation of statistical language models for applications such as speech recognition. Offered free of charge for non-commercial use, SRILM's capabilities encompass the creation and assessment of diverse language model types rooted in N-gram statistics. Additionally, it supports related tasks, including statistical tagging and the handling of N-best lists and word lattices. This paper provides an overview of SRILM's features, delving into its design and implementation. Emphasis is placed on the toolkit's ease of use for rapid prototyping, the reusability of its components, and the seamless integration of various tools.

SRILM: An Open-Source Language Modeling Toolkit for Rapid Prototyping
SRILM is an open-source toolkit developed to streamline the process of creating and experimenting with statistical language models, particularly for speech recognition. Accessible for non-commercial use, SRILM comprises a suite of C++ libraries, executable programs, and scripts. The toolkit offers support for the development and evaluation of a range of language models based on N-gram statistics, alongside functions for statistical tagging and the management of N-best lists and word lattices. This paper outlines the toolkit's features, discussing its design philosophy, which prioritizes ease of use for rapid prototyping, the reusability of its components, and the compatibility of tools for a comprehensive modeling experience.

Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this paper, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of task scheduling policies. To support this claim, we describe such an implementation called the Galois system.
 We demonstrate the capabilities of this infrastructure in three ways. First, we implement more sophisticated algorithms for some of the graph analytics problems tackled by previous DSLs and show that end-to-end performance can be improved by orders of magnitude even on power-law graphs, thanks to the better algorithms facilitated by a more general programming model. Second, we show that, even when an algorithm can be expressed in existing DSLs, the implementation of that algorithm in the more general system can be orders of magnitude faster when the input graphs are road networks and similar graphs with high diameter, thanks to more sophisticated scheduling. Third, we implement the APIs of three existing graph DSLs on top of the common infrastructure in a few hundred lines of code and show that even for power-law graphs, the performance of the resulting implementations often exceeds that of the original DSL systems, thanks to the lightweight infrastructure.

We introduce the Residual Memory Network (RMN) architecture to language modeling. RMN is an architecture of feedforward neural networks that incorporates residual connections and time-delay connections that allow us to naturally incorporate information from a substantial time context. As this is the first time RMNs are applied for language modeling, we thoroughly investigate their behaviour on the well studied Penn Treebank corpus. We change the model slightly for the needs of language modeling, reducing both its time and memory consumption. Our results show that RMN is a suitable choice for small-sized neural language models: With test perplexity 112.7 and as few as 2.3M parameters, they out-perform both a much larger vanilla RNN (PPL 124, 8M parameters) and a similarly sized LSTM (PPL 115, 2.08M parameters), while being only by less than 3 perplexity points worse than twice as big LSTM.

Recently, many NLP tasks have benefited from distributed word representation. However, it remains unknown whether embedding models are really immune to the typological diversity of languages, despite the language-independent architecture. Here we investigate three representative models on a large set of language samples by mapping dense embedding to sparse linguistic property space. Experiment results reveal the language universal and specific properties encoded in various word representation. Additionally, strong evidence supports the utility of word form, especially for inflectional languages.

Languages with rich inflectional morphology exhibit lexical data sparsity, since the word used to express a given concept will vary with the syntactic context. For instance, each count noun in Czech has 12 forms (where English uses only singular and plural). Even in large corpora, we are unlikely to observe all inflections of a given lemma. This reduces the vocabulary coverage of methods that induce continuous representations for words from distributional corpus information. We solve this problem by exploiting existing morphological resources that can enumerate a word’s component morphemes. We present a latentvariable Gaussian graphical model that allows us to extrapolate continuous representations for words not observed in the training corpus, as well as smoothing the representations provided for the observed words. The latent variables represent embeddings of morphemes, which combine to create embeddings of words. Over several languages and training sizes, our model improves the embeddings for words, when evaluated on an analogy task, skip-gram predictive accuracy, and word similarity.

In this paper we describe a work in progress on designing the continuous vector space word representations able to map unseen data adequately. We propose a LSTM-based feature extraction layer that reads in a sequence of characters corresponding to a word and outputs a single fixed-length real-valued vector. We then test our model on a POS tagging task on four typologically different languages. The results of the experiments suggest that the model can offer a solution to the out-of-vocabulary words problem, as in a comparable setting its OOV accuracy improves over that of a state of the art tagger.

Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions. We propose a novel hierarchical model for structured prediction over sequences and trees which exploits global context by conditioning each generation decision on an unbounded context of prior decisions. This builds on the success of Markov models but without imposing a fixed bound in order to better represent global phenomena. To facilitate learning of this large and unbounded model, we use a hierarchical PitmanYor process prior which provides a recursive form of smoothing. We propose prediction algorithms based on A* and Markov Chain Monte Carlo sampling. Empirical results demonstrate the potential of our model compared to baseline finite-context Markov models on part-of-speech tagging and syntactic parsing.

Efficient computation of n-gram posterior probabilities from lattices has applications in lattice-based minimum Bayes-risk decoding in statistical machine translation and the estimation of expected document frequencies from spoken corpora. In this paper, we present an algorithm for computing the posterior probabilities of all ngrams in a lattice and constructing a minimal deterministic weighted finite-state automaton associating each n-gram with its posterior for efficient storage and retrieval. Our algorithm builds upon the best known algorithm in literature for computing ngram posteriors from lattices and leverages the following observations to significantly improve the time and space requirements: i) the n-grams for which the posteriors will be computed typically comprises all n-grams in the lattice up to a certain length, ii) posterior is equivalent to expected count for an n-gram that do not repeat on any path, iii) there are efficient algorithms for computing n-gram expected counts from lattices. We present experimental results comparing our algorithm with the best known algorithm in literature as well as a baseline algorithm based on weighted finite-state automata operations.

Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.

In this paper, we introduce a Web-scale linguistics search engine, Linggle, that retrieves lexical bundles in response to a given query. The query might contain keywords, wildcards, wild parts of speech (PoS), synonyms, and additional regular expression (RE) operators. In our approach, we incorporate inverted file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword retrieval commands. Word chunks are retrieved with counts, further filtering the chunks with the query as a RE, and finally displaying the results according to the counts, similarities, and topics. Clusters of synonyms or conceptually related words are also provided. In addition, Linggle provides example sentences from The New York Times on demand. The current implementation of Linggle is the most functionally comprehensive, and is in principle language and dataset independent. We plan to extend Linggle to provide fast and convenient access to a wealth of linguistic information embodied in Web scale datasets including Google Web 1T and Google Books Ngram for many major languages in the world.

We describe a new formalism for storing linguistic data in a text to speech system. Linguistic entities such as words and phones are stored as feature structures in a general object called an linguistic item. Items are configurable at run time and via the feature structure can contain arbitrary information. Linguistic relations are used to store the relationship between items of the same linguistic type. Relations can take any graph structure but are commonly trees or lists. Utterance structures contain all the items and relations contained in a single utterance. We first describe the design goals when building a synthesis architecture, and then describe some problems with previous architectures. We then discuss our new formalism in general along with the implementation details and consequences of our approach.


Question: Factored language models and the utilization of generalized parallel backoff.

This paper delves into the intricacies of structuring probabilistic dependencies within the framework of stochastic language modeling. It explores how these dependencies can be effectively organized to enhance the predictive capabilities of language models. Unlike deterministic models, stochastic models incorporate randomness to capture the inherent uncertainty in language data. By carefully designing the probabilistic relationships between words or linguistic units, these models can more accurately predict the likelihood of various linguistic sequences. The paper discusses various techniques for structuring these dependencies, including the use of n-gram models, neural network architectures, and Bayesian networks. It also examines how these structures can be optimized to balance the trade-off between model complexity and predictive accuracy. Through empirical evaluations, the paper highlights the benefits of well-structured probabilistic dependencies in improving the overall performance of stochastic language models. This research is particularly relevant in the context of natural language processing tasks, where accurate language modeling is crucial for tasks such as speech recognition, machine translation, and text generation.

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU, respectively.

We introduce a (semi-)automatic OCR post-processing system that utilizes web-scale linguistic corpora in providing high-quality correction. This paper is a comprehensive system overview with the focus on the computational procedures, applied linguistic analysis, and processing optimization.

Statistical machine translation (SMT) performance suffers when models are trained on only small amounts of parallel data. The learned models typically have both low accuracy (incorrect translations and feature scores) and low coverage (high out-of-vocabulary rates). In this work, we use an additional data resource, comparable corpora, to improve both. Beginning with a small bitext and corresponding phrase-based SMT model, we improve coverage by using bilingual lexicon induction techniques to learn new translations from comparable corpora. Then, we supplement the model’s feature space with translation scores estimated over comparable corpora in order to improve accuracy. We observe improvements between 0.5 and 1.7 BLEU translating Tamil, Telugu, Bengali, Malayalam, Hindi, and Urdu into English.

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long shortterm memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-ofthe-art despite having 60% fewer parameters. On languages with rich morphology (Czech, German, French, Spanish, Russian), the model consistently outperforms a Kneser-Ney baseline and word-level/morpheme-level LSTM baselines, again with far fewer parameters. Our results suggest that on many languages, character inputs are sufficient for language modeling.

Memory-Based Learning (MBL) is based on the storage of all available training data, and similarity-based reasoning for handling new cases. By interpreting tasks such as POS tagging and shallow parsing as classification tasks, the advantages of MBL (implicit smoothing of sparse data, automatic integration and relevance weighting of information sources, handling exceptional data) contribute to state-of-the-art accuracy. However, Hidden Markov Models (HMM) typically achieve higher accuracy than MBL (and other Machine Learning approaches) for tasks such as POS tagging and chunking. In this paper, we investigate how the advantages of MBL, such as its potential to integrate various sources of information, come to play when we compare our approach to HMMs on two Information Extraction (IE) datasets: the well-known Seminar Announcement data set and a new German Curriculum Vitae data set. 1 Memory-Based Language Processing Memory-Based Learning (MBL) is a supervised classification-based learning method. A vector of feature values (an instance) is associated with a class by a classifier that lazily extrapolates from the most similar set (nearest neighbors) selected from all stored training examples. This is in contrast to eager learning methods like decision tree learning [26], rule induction [9], or Inductive Logic Programming [7], which abstract a generalized structure from the training set beforehand (forgetting the examples themselves), and use that to derive a classification for a new instance. In MBL, a distance metric on the feature space defines what are the nearest neighbors of an instance. Metrics with feature weights based on information-theory or other relevance statistics allow us to use rich representations of instances and their context, and to balance the influences of diverse information sources in computing distance. Natural Language Processing (NLP) tasks typically concern the mapping of an input representation (e.g., a series of words) into an output representation (e.g., the POS tags corresponding to each word in the input). Most NLP tasks can therefore easily be interpreted as sequences of classification 34 Zavrel, Daelemans tasks: e.g., given a word and some representation of its context, decide what tag to assign to each word in its context. By creating a separate classification instance (a “moving window” approach) for each word and its context, shallow syntactic or semantic structures can be produced for whole sentences or texts. In this paper, we argue that more semantic and complex input-output mappings, such as Information Extraction, can also effectively be modeled by such a Memory-based classification-oriented framework, and that this approach has a number of very interesting advantages over rivalling methods, most notably that each classification decision can be made dependent on a very rich and diverse set of features. The properties of MBL as a lazy, similarity-based learning method seem make a good fit to the properties of typical disambiguation problems in NLP: • Similar input representations lead to similar output. E.g., words occurring in a similar context in general have the same POS tag. Similarity-based reasoning is the core of MBL. • Many sub-generalizations and exceptions. By keeping in memory all training instances, exceptions included, an MBL approach can capture generalization from exceptional or low-frequency cases according to [12]. • Need for integration of diverse types of information. E.g., in Information Extraction, lexical features, spelling features, syntactic as well as phrasal context features, global text structure, and layout features can potentially be very relevant. • Automatic smoothing in very rich event spaces. Supervised learning of NLP tasks regularly runs into problems of sparse data; not enough training data is available to extract reliable parameters for complex models. MBL incorporates an implicit robust form of smoothing by similarity [33]. In the remainder of this Section, we will show how a memory-, similarity-, and classification-based approach can be applied to shallow syntactic parsing, and can lead to state-of-the-art accuracy. Most of the tasks discussed here can also easily be modeled using Hidden Markov Models (HMM), and often with surprising accuracy. We will discuss the strengths of the HMMs and draw a comparison between the classification-based MBL method and the sequence-optimizing HMM approach (Section 1.2). 1.1 Memory-Based Shallow Parsing Shallow parsing is an important component of most text analysis systems in Text Mining applications such as information extraction, summary generation, and question answering. It includes discovering the main constituents of sentences (NPs, VPs, PPs) and their heads, and determining syntactic relationships like subject, object, adjunct relations between verbs and heads of other constituents. This is an important first step to understanding the who, what, when, and where of sentences in a text. Feature-Rich Memory-Based Classification for Information Extraction 35 In our approach to memory-based shallow parsing, we carve up the syntactic analysis process into a number of classification tasks with input vectors representing a focus item and a dynamically selected surrounding context. These classification tasks can be segmentation tasks (e.g., decide whether a focus word or tag is the start or end of an NP) or disambiguation tasks (e.g., decide whether a chunk is the subject NP, the object NP or neither). Output of some memory-based modules is used as input by other memory-based modules (e.g., a tagger feeds a chunker and the latter feeds a syntactic relation assignment module). Similar ideas about cascading of processing steps have also been explored in other approaches to text analysis: e.g., finite state partial parsing [1,18], statistical decision tree parsing [23], and maximum entropy parsing [30]. The approach briefly described here is explained and evaluated in more detail in [10,11,6] . Chunking The phrase chunking task can be defined as a classification task by generalizing the approach of [28], who proposed to convert NP-chunking to tagging each word with I for a word inside an NP, O for outside an NP, and B for between two NPs). The decision on these so called IOB tags for a word can be made by looking at the Part-of-Speech tag and the identity of the focus word and its local context. For the more general task of chunking other non-recursive phrases, we simply extend the tag set with IOB tags for each type of phrase. To illustrate this encoding with the extended IOB tag set, we can tag the sentence: But/CC [NP the/DT dollar/NN NP] [ADVP later/RB ADVP] [VP rebounded/VBD VP] ,/, [VP finishing/VBG VP] [ADJP slightly/RB higher/R ADJP] [Prep against/IN Prep] [NP the/DT yen/NNS NP] [ADJP although/IN ADJP] [ADJP slightly/RB lower/JJR ADJP] [Prep against/IN Prep] [NP the/DT mark/NN NP] ./. as: But/CCO the/DTI−NP dollar/NNI−NP later/RBI−ADV P rebounded/VBDI−V P ,/,O finishing/VBGI−V P slightly/RBI−ADV P higher/RBRI−ADV P against/INI−Prep the/DTI−NP yen/NNSI−NP although/INI−ADJP slightly/RBB−ADJP lower/JJRI−ADJP against/INI−Prep the/DTI−NP mark/NNI−NP ./.O Table 1 (from [6]) shows the accuracy of this memory-based chunking approach when training and testing on Wall Street Journal material. We report Precision, Recall, and Fβ=1 scores, a weighted harmonic mean of Recall and Precision (Fβ = (β+1)∗P∗R β2∗P+R) ). 1 An online demonstration of the Memory-Based Shallow Parser can be found at http://ilk.kub.nl . 36 Zavrel, Daelemans type precision recall Fβ=1 NPchunks 92.5 92.2 92.3 VPchunks 91.9 91.7 91.8 ADJPchunks 68.4 65.0 66.7 ADVPchunks 78.0 77.9 77.9 Prepchunks 95.5 96.7 96.1 PPchunks 91.9 92.2 92.0 ADVFUNCs 78.0 69.5 73.5 Table 1. Results of chunking–labeling experiments. Reproduced from [6]. Grammatical Relation Finding After POS tagging, phrase chunking and labeling, the last step of the shallow parsing consists of resolving the (types of) attachment between labeled phrases. This is done by using a classifier to assign a grammatical relation (GR) between pairs of words in a sentence. In our approach, one of these words is always a verb, since this yields the most important GRs. The other word (focus) is the head of the phrase which is annotated with this grammatical relation in the treebank (e.g., a noun as head of an NP). An instance for such a pair of words is constructed by extracting a set of feature values from the sentence. The instance contains information about the verb and the focus: a feature for the word form and a feature for the POS of both. It also has similar features for the local context of the focus. Experiments on the training data suggest an optimal context width of two words to the left and one to the right. In addition to the lexical and the local context information, superficial information about clause structure was included: the distance from the verb to the focus, counted in words. A negative distance means that the focus is to the left of the verb. Other features contain the number of other verbs between the verb and the focus, and the number of intervening commas. These features were chosen by manual “feature engineering”. Table 2 shows some of the feature-value instances corresponding to the following sentence (POS tags after the slash, chunks denoted with square and curly brackets, and adverbial functions after the dash): [ADVP Not /RB surprisingly /RB ADVP] ,/, [NP Peter /NNP Miller /NNP NP] ,/, [NP who /WP NP] [VP organized /VBD VP] [NP the /DT conference /NN NP] {PP-LOC [Prep in /IN Prep] [NP New /NNP York /NNP NP] PP-LOC} ,/, [VP does /VBZ not /RB want /VB to /TO come /VB VP] {PP-DIR [Prep to /IN Prep] [NP Paris /NNP NP] PP-DIR} [Prep without /IN Prep] [VP bringing /VBG VP] [NP his /PRP$ wife /NN NP]. Table 3 shows the results of the experiments. In the first row, only POS tag features are used. Other rows show the results when adding several types of chunk informati

Recent approaches to text classification have used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to clarify the confusion by describing the differences and details of these two models, and by empirically comparing their classification performance on five text corpora. We find that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes—providing on average a 27% reduction in error over the multi-variate Bernoulli model at any vocabulary size.

In this work, we study the impact of the word order decoding direction for statistical machine translation (SMT). Both phrase-based and hierarchical phrasebased SMT systems are investigated by reversing the word order of the source and/or target language and comparing the translation results with the normal direction. Analysis are done on several components such as alignment model, language model and phrase table to see which of them accounts for the differences generated by various translation directions. Furthermore, we propose to use system combination, alignment combinations and phrase table combinations to take benefit from systems trained with different translation directions. Experimental results show improvements of up to 1.7 points in BLEU and 3.1 points in TER compared to the normal direction systems for the NTCIR9 Japanese-English and Chinese-English tasks.

Joint models of syntactic and semantic parsing have the potential to improve performance on both tasks—but to date, the best results have been achieved with pipelines. We introduce a joint model using CCG, which is motivated by the close link between CCG syntax and semantics. Semantic roles are recovered by labelling the deep dependency structures produced by the grammar. Furthermore, because CCG is lexicalized, we show it is possible to factor the parsing model over words and introduce a new A∗ parsing algorithm— which we demonstrate is faster and more accurate than adaptive supertagging. Our joint model is the first to substantially improve both syntactic and semantic accuracy over a comparable pipeline, and also achieves state-of-the-art results for a nonensemble semantic role labelling model.

In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389

While Deep Neural Networks (DNNs) have achieved tremendous success for large vocabulary continuous speech recognition (LVCSR) tasks, training of these networks is slow. One reason is that DNNs are trained with a large number of training parameters (i.e., 10-50 million). Because networks are trained with a large number of output targets to achieve good performance, the majority of these parameters are in the final weight layer. In this paper, we propose a low-rank matrix factorization of the final weight layer. We apply this low-rank technique to DNNs for both acoustic modeling and language modeling. We show on three different LVCSR tasks ranging between 50-400 hrs, that a low-rank factorization reduces the number of parameters of the network by 30-50%. This results in roughly an equivalent reduction in training time, without a significant loss in final recognition accuracy, compared to a full-rank representation.


Question: Factored language models and the application of generalized parallel backoff.

This paper introduces a novel methodology for the construction of random fields from a collection of training data, focusing on the development of increasingly intricate models. The proposed learning framework facilitates the evolution of complexity in these fields by incorporating potential functions, or features, that are anchored within progressively larger subgraphs. A key component of this approach involves the assignment of weights to each feature, optimized through the minimization of the Kullback-Leibler divergence. This divergence quantifies the dissimilarity between the model's distribution and the empirical distribution derived from the training samples. A greedy algorithm is employed to sequentially incorporate new features into the random field, while an iterative scaling algorithm is used to ascertain the optimal weight values. The distinguishing characteristic of the random fields developed herein lies in their non-Markovian nature and the substantial number of parameters they entail, setting them apart from models typically encountered in computer vision literature. The paper also draws parallels with alternative learning strategies, such as decision trees and Boltzmann machines. As an illustration of the technique's efficacy, an application to the challenge of automated word classification in natural language processing is provided, highlighting its potential in this domain.

We propose a new fast purely discriminative algorithm for natural language parsing, based on a “deep” recurrent convolutional graph transformer network (GTN). Assuming a decomposition of a parse tree into a stack of “levels”, the network predicts a level of the tree taking into account predictions of previous levels. Using only few basic text features which leverage word representations from Collobert and Weston (2008), we show similar performance (in F1 score) to existing pure discriminative parsers and existing “benchmark” parsers (like Collins parser, probabilistic context-free grammars based), with a huge speed advantage.

Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedyand only explores a tiny fraction of the whole space (even with beam search) as opposed to dynamic programming. We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging “equivalent” stacks based on feature values. Empirically, our algorithm yields up to a five-fold speedup over a state-of-the-art shift-reduce dependency parser with no loss in accuracy. Better search also leads to better learning, and our final parser outperforms all previously reported dependency parsers for English and Chinese, yet is much faster.

Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: contextsensitive spelling correction. This is the task of xing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classi ers. We nd that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. In Machine Learning: Proceedings of the 13th International Conference, Lorenza Saitta, ed., Morgan Kaufmann, San Francisco, CA, 1996, pages 182{190. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1996 201 Broadway, Cambridge, Massachusetts 02139 Dept. of Appl. Math. & CS, Weizmann Institute of Science, Rehovot 76100, Israel. 1. First printing, TR96-07, April 1996

Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a small number of dense features, it can work very fast, while achieving an about 2% improvement in unlabeled and labeled attachment scores on both English and Chinese datasets. Concretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank.

We propose a general class of language models that treat reference as explicit stochastic latent variables. This architecture allows models to create mentions of entities and their attributes by accessing external databases (required by, e.g., dialogue generation and recipe generation) and internal state (required by, e.g. language models which are aware of coreference). This facilitates the incorporation of information that can be accessed in predictable locations in databases or discourse context, even when the targets of the reference may be rare words. Experiments on three representative applications show our model variants outperform models based on deterministic attention.

Wepresentanounphrasecoreferencesystem that extends the work of Soon et al. (2001) and, to our knowledge, producesthebestresultsto dateontheMUC6 andMUC-7 coreferenceresolutiondata sets— F-measuresof 70.4 and63.4, respecti vely. Improvementsarisefrom two sources: extra-linguistic changesto the learningframework anda large-scaleexpansionof thefeaturesetto includemore sophisticatedlinguisticknowledge.

In generaltext-to-speechsystems, it is notpossibleto guaranteethat a lexiconwill containall wordsfoundin a text, thereforesomesystemfor predictingpronunciationfrom theword itself is necessary . Herewe presenta generalframework for building letter to sound (LTS) rulesfrom a word list in a language.The techniquecanbe fully automatic,thougha small amountof handseedingcangive betterresults.We have appliedthis techniqueto English(UK and US), Frenchand German. The generatedmodelsachieve, 75%, 58%, 93% and89%, respecti vely, wordscorrectfor held out data from theword lists. To testour modelson moretypical datawe alsoanalyzedgeneral text, to find which wordsdo not appearin our lexicon. Theseunknown wordswereusedasamorerealistictestcorpusfor ourmodels. We also discussthe distribution and type of suchunknown words.

We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-ofthe-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-ofthe-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.

We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions. Disciplines Computer Sciences Comments Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by agreement. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL '06). Association for Computational Linguistics, Stroudsburg, PA, USA, 104-111. DOI=10.3115/1220835.1220849 http://dx.doi.org/10.3115/1220835.1220849 © ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, {(2006)} http://doi.acm.org/10.3115/1220835.1220849" Email permissions@acm.org This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/533 Alignment by Agreement Percy Liang UC Berkeley Berkeley, CA 94720 pliang@cs.berkeley.edu Ben Taskar UC Berkeley Berkeley, CA 94720 taskar@cs.berkeley.edu Dan Klein UC Berkeley Berkeley, CA 94720 klein@cs.berkeley.edu

Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.


Question: Factored language models and the application of generalized parallel backoff.

The CMU-Cambridge Statistical Language Modeling Toolkit: Enhanced Features and Efficiency
The CMU-Cambridge Statistical Language Modeling toolkit, released to the public to simplify the creation and assessment of bigram and trigram language models, has gained widespread adoption across numerous academic, government, and industrial laboratories worldwide. This paper introduces an updated version of the toolkit, highlighting its advancements. We first review the conventional language modeling techniques implemented in the toolkit, focusing on bigram and trigram models. Subsequently, we detail the enhanced efficiency and additional features of the new toolkit compared to previous software solutions for language modeling. Lastly, we provide a practical illustration of the toolkit's application in building and evaluating a basic language model, demonstrating its utility and ease of use. The toolkit's improved functionality and efficiency make it a valuable resource for researchers and practitioners in the field of statistical language modeling.

Though designing of classifies for Indic script handwriting recognition has been researched with enough attention, use of language model has so far received little exposure. This paper attempts to develop a weighted finite-state transducer (WFST) based language model for improving the current recognition accuracy. Both the recognition hypothesis (i.e. the segmentation lattice) and the lexicon are modeled as two WFSTs. Concatenation of these two FSTs accept a valid word(s) which is (are) present in the recognition lattice. A third FST called error FST is also introduced to retrieve certain words which were missing in the previous concatenation operation. The proposed model has been tested for online Bangla handwriting recognition though the underlying principle can equally be applied for recognition of offline or printed words. Experiment on a part of ISI-Bangla handwriting database shows that while the present classifiers (without using any language model) can recognize about 73% word, use of recognition and lexicon FSTs improve this result by about 9% giving an average word-level accuracy of 82%. Introduction of error FST further improves this accuracy to 93%. This remarkable improvement in word recognition accuracy by using FST-based language model would serve as a significant revelation for the research in handwriting recognition, in general and Indic script handwriting recognition, in particular.

In automatic speech recognition on mobile devices, very often what a user says strongly depends on the particular context he or she is in. The n-grams relevant to the context are often not known in advance. The context can depend on, for example, particular dialog state, options presented to the user, conversation topic, location, etc. Speech recognition of sentences that include these n-grams can be challenging, as they are often not well represented in a language model (LM) or even include out-of-vocabulary (OOV) words. In this paper, we propose a solution for using contextual information to improve speech recognition accuracy. We utilize an on-the-fly rescoring mechanism to adjust the LM weights of a small set of n-grams relevant to the particular context during speech decoding. Our solution handles out of vocabulary words. It also addresses efficient combination of multiple sources of context and it even allows biasing class based language models. We show significant speech recognition accuracy improvements on several datasets, using various types of contexts, without negatively impacting the overall system. The improvements are obtained in both offline and live experiments.

This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology of a natural language. A probabilistic maximum a posteriori model is utilized, which builds hierarchical representations for a set of morphs, which are morpheme-like units discovered from unannotated text corpora. The induced morph lexicon stores parameters related to both the “meaning” and “form” of the morphs it contains. These parameters affect the role of the morphs in words. The model is implemented in a task of unsupervised morpheme segmentation of Finnish and English words. Very good results are obtained for Finnish and almost as good results are obtained in the English task.

In this paper we examine language modeling for text simplification. Unlike some text-to-text translation tasks, text simplification is a monolingual translation task allowing for text in both the input and output domain to be used for training the language model. We explore the relationship between normal English and simplified English and compare language models trained on varying amounts of text from each. We evaluate the models intrinsically with perplexity and extrinsically on the lexical simplification task from SemEval 2012. We find that a combined model using both simplified and normal English data achieves a 23% improvement in perplexity and a 24% improvement on the lexical simplification task over a model trained only on simple data. Post-hoc analysis shows that the additional unsimplified data provides better coverage for unseen and rare n-grams.

WordNet is perhaps the most important and widely used lexical resource for natural language processing systems up to now. WordNet: An Electronic Lexical Database, edited by Christiane Fellbaum, discusses the design of WordNet from both theoretical and historical perspectives, provides an up-to-date description of the lexical database, and presents a set of applications of WordNet. The book contains a foreword by George Miller, an introduction by Christiane Fellbaum, seven chapters from the Cognitive Sciences Laboratory of Princeton University, where WordNet was produced, and nine chapters contributed by scientists from elsewhere. Miller's foreword offers a fascinating account of the history of WordNet. He discusses the presuppositions of such a lexical database, how the top-level noun categories were determined, and the sources of the words in WordNet. He also writes about the evolution of WordNet from its original incarnation as a dictionary browser to a broad-coverage lexicon, and the involvement of different people during its various stages of development over a decade. It makes very interesting reading for casual and serious users of WordNet and anyone who is grateful for the existence of WordNet. The book is organized in three parts. Part I is about WordNet itself and consists of four chapters: "Nouns in WordNet" by George Miller, "Modifiers in WordNet" by Katherine Miller, "A semantic network of English verbs" by Christiane Fellbaum, and "Design and implementation of the WordNet lexical database and search software" by Randee Tengi. These chapters are essentially updated versions of four papers from Miller (1990). Compared with the earlier papers, the chapters in this book focus more on the underlying assumptions and rationales behind the design decisions. The description of the information contained in WordNet, however, is not as detailed as in Miller (1990). The main new additions in these chapters include an explanation of sense grouping in George Miller's chapter, a section about adverbs in Katherine Miller's chapter, observations about autohyponymy (one sense of a word being a hyponym of another sense of the same word) and autoantonymy (one sense of a word being an antonym of another sense of the same word) in Fellbaum's chapter, and Tengi's description of the Grinder, a program that converts the files the lexicographers work with to searchable lexical databases. The three papers in Part II are characterized as "extensions, enhancements and

Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.

We describe a new formalism for storing linguistic data in a text to speech system. Linguistic entities such as words and phones are stored as feature structures in a general object called an linguistic item. Items are configurable at run time and via the feature structure can contain arbitrary information. Linguistic relations are used to store the relationship between items of the same linguistic type. Relations can take any graph structure but are commonly trees or lists. Utterance structures contain all the items and relations contained in a single utterance. We first describe the design goals when building a synthesis architecture, and then describe some problems with previous architectures. We then discuss our new formalism in general along with the implementation details and consequences of our approach.

This chapter describes a general representation and algorithmic framework for speech recognition based on weighted finite-state transducers. These transducers provide a common and natural representation for major components of speech recognition systems, including hidden Markov models (HMMs), context-dependency models, pronunciation dictionaries, statistical grammars, and word or phone lattices. General algorithms for building and optimizing transducer models are presented, including composition for combining models, weighted determinization and minimization for optimizing time and space requirements, and a weight pushing algorithm for redistributing transition weights optimally for speech recognition. The application of these methods to large-vocabulary recognition tasks is explained in detail, and experimental results are given, in particular for the North American Business News (NAB) task, in which these methods were used to combine HMMs, full cross-word triphones, a lexicon of forty thousand words, and a large trigram grammar into a single weighted transducer that is only somewhat larger than the trigram word grammar and that runs NAB in real-time on a very simple decoder. Another example demonstrates that the same methods can be used to optimize lattices for second-pass recognition.

We present a fast compression technique for natural language texts. The novelties are that (1) decompression of arbitrary portions of the text can be done very efficiently, (2) exact search for words and phrases can be done on the compressed text directly, using any known sequential pattern-matching algorithm, and (3) word-based approximate and extended search can also be done efficiently without any decoding. The compression scheme uses a semistatic word-based model and a Huffman code where the coding alphabet is byte-oriented rather than bit-oriented. We compress typical English texts to about 30% of their original size, against 40% and 35% for Compress and Gzip, respectively. Compression time is close to that of Compress and approximately half of the time of Gzip, and decompression time is lower than that of Gzip and one third of that of Compress. We present three algorithms to search the compressed text. They allow a large number of variations over the basic word and phrase search capability, such as sets of characters, arbitrary regular expressions, and approximate matching. Separators and stopwords can be discarded at search time without significantly increasing the cost. When searching for simple words, the experiments show that running our algorithms on a compressed text is twice as fast as running the best existing software on the uncompressed version of the same text. When searching complex or approximate patterns, our algorithms are up to 8 times faster than the search on uncompressed text. We also discuss the impact of our technique in inverted files pointing to logical blocks and argue for the possibility of keeping the text compressed all the time, decompressing only for displaying purposes.

In this survey, we will discuss current uses of finite-state information in several statistical natural language processing tasks. To this end, we will review standard approaches in tokenization, part-of-speech tagging, and parsing, and illustrate the utility of finite-state information and technology in these areas. The particular problems were chosen to allow a natural progression from simple prediction to structured prediction. We aim for a sufficiently formal presentation suitable for readers with a background in automata theory that allows to appreciate the contribution of finite-state approaches, but we will not discuss practical issues outside the core ideas. We provide instructive examples and pointers into the relevant literature for all constructions. We close with an outlook on finite-state technology in statistical machine translation.