Question: The connection between personality characteristics and counterproductive work actions: the mediating impact of job satisfaction.

The Impact of Personality Traits on Significant Life Outcomes: A Comprehensive Analysis

Personality significantly influences various aspects of life, with measurable effects on individual, interpersonal, and societal levels. The Big Five personality traits serve as a useful framework for exploring these impacts. At the individual level, personality characteristics are linked to subjective well-being, mental and physical health, personal values, and self-concept. Interpersonally, these traits affect the dynamics of relationships with friends, family, and romantic partners, shaping the quality and longevity of these connections. At a broader societal level, personality dispositions have implications for career decisions, job satisfaction, and performance, as well as engagement in community activities, adherence to laws, and political beliefs. This analysis underscores the pivotal role of personality in determining a wide range of life outcomes, from personal happiness to societal participation.

**Question**: THE IMPACT OF EMOTIONAL INTELLIGENCE ON LEADERSHIP EFFECTIVENESS IN THE HEALTHCARE SECTOR

**Documents**: Emotional Intelligence: Issues and Insights
Emotional intelligence (EI) has gained considerable attention in recent years, with research highlighting its role in various aspects of life, including leadership effectiveness. This paper explores the concept of EI, examining its components and the ways in which it contributes to leadership success. We discuss the measurement of EI, the challenges in assessing it accurately, and the implications of high EI for leadership behaviors and outcomes. Furthermore, we address the limitations of EI research and propose areas for future investigation. Our review suggests that EI is a critical factor in leadership effectiveness, particularly in the healthcare sector, where emotional and interpersonal skills are essential for managing diverse teams and providing patient-centered care.

**Similar Documents**: Emotional Intelligence: A Key Determinant of Leadership Success in Healthcare

In recent years, the significance of emotional intelligence (EI) in leadership has become increasingly evident, particularly in the healthcare sector. This paper delves into the construct of EI, analyzing its components and their relevance to leadership effectiveness. We scrutinize the methodologies for measuring EI, discussing the complexities involved in its accurate assessment, and the benefits of high EI for leadership performance. The paper also identifies gaps in current EI research and outlines potential avenues for future study. Our findings emphasize that EI plays a crucial role in enhancing leadership capabilities, especially in healthcare environments where emotional competencies and interpersonal skills are vital for team management and delivering patient-centered services.

Although both downsizing and voluntary turnover have been topics of great interest in the organizational literature, little research has addressed the possible relationship between the two. Using organization-level data from multiple industries, we first investigate whether downsizing predicts voluntary turnover rates. Second, to lend support to our causal model, we examine whether this relationship is mediated by aggregated levels of organizational commitment. Third, we test whether the downsizing-turnover rate relationship: (1) is mitigated by HR practices that tend to embed employees in the organization or convey procedural fairness; and (2) is strengthened by HR practices that enhance career development. Results support the hypothesized main, mediated, and moderated effects.

PURPOSE
Studies of high-performing organisations have consistently reported a positive relationship between high performance work systems (HPWS) and performance outcomes. Although many of these studies have been conducted in manufacturing, similar findings of a positive correlation between aspects of HPWS and improved care delivery and patient outcomes have been reported in international health care studies. The purpose of this paper is to bring together the results from a series of studies conducted within Australian health care organisations. First, the authors seek to demonstrate the link found between high performance work systems and organisational performance, including the perceived quality of patient care. Second, the paper aims to show that the hospitals studied do not have the necessary aspects of HPWS in place and that there has been little consideration of HPWS in health system reform.

DESIGN/METHODOLOGY/APPROACH
The paper draws on a series of correlation studies using survey data from hospitals in Australia, supplemented by qualitative data collection and analysis. To demonstrate the link between HPWS and perceived quality of care delivery the authors conducted regression analysis with tests of mediation and moderation to analyse survey responses of 201 nurses in a large regional Australian health service and explored HRM and HPWS in detail in three casestudy organisations. To achieve the second aim, the authors surveyed human resource and other senior managers in all Victorian health sector organisations and reviewed policy documents related to health system reform planned for Australia.

FINDINGS
The findings suggest that there is a relationship between HPWS and the perceived quality of care that is mediated by human resource management (HRM) outcomes, such as psychological empowerment. It is also found that health care organisations in Australia generally do not have the necessary aspects of HPWS in place, creating a policy and practice gap. Although the chief executive officers of health service organisations reported high levels of strategic HRM, the human resource and other managers reported a distinct lack of HPWS from their perspectives. The authors discuss why health care organisations may have difficulty in achieving HPWS.

ORIGINALITY/VALUE
Leaders in health care organisations should focus on ensuring human resource management systems, structures and processes that support HPWS. Policy makers need to consider HPWS as a necessary component of health system reform. There is a strong need to reorient organisational human resource management policies and procedures in public health care organisations towards high performing work systems.

The concept of work exhaustion (or job burnout) from the management and psychology research literature is examined in the context of technology professionals. Data were collected from 270 IT professionals and managers in various industries across the United States. Through structural equation modeling, work exhaustion was shown to partially mediate the effects of workplace factors on turnover intention. In addition, the results of the study revealed that: (1) technology professionals experiencing higher levels of exhaustion reported higher intentions to leave the job and, (2) of the variables expected to influence exhaustion (work overload, role ambiguity and conflict, lack of autonomy and lack of rewards), work overload was the strongest contributor to exhaustion in the technology workers. Moreover, exhausted IT professionals identified insufficient staff and resources as a primary cause of work overload and exhaustion. Implications for practice and future research are discussed.

In this paper, the impact of gender and Big Five personality factors (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism) on visual media preferences (for comedy, horror, action, romance and fantasy film genres) was examined. The analyses were carried out using data collected by Stillwell’s (2007) myPersonality application on Facebook; and focused on a sub-sample of British residents aged 16-25 (n=29,197). It was confirmed that females prefer romantic films, whereas males favour action films. As predicted, there was also a marked interaction between gender and personality characteristics: females score higher on Neuroticism and Agreeableness than males. Individuals scoring high on the dimension of Openness were more likely to prefer comedy and fantasy films than romantic films. Conscientious individuals were more inclined to enjoy action and romance genres. Individuals scoring high on Neuroticism tended to prefer romantic films. Finally, significant interactions were noted between gender and Openness with regard to preferences for comedy, action and romance genres. Overall, it is certain that the combination of gender and Big Five personality factors contributes substantially to an understanding of people’s film preferences. GENDER, PERSONALITY AND FILM PREFERENCES 3 Assessing the impact of Gender and Personality on Film Preferences Do comedies have a sexually differentiated public? Are males and females who enjoy horror films similar in terms of personality? Regardless of gender, do personality characteristics influence preference for the fantasy genre? In the present study, we have attempted to provide a meaningful answer to the aforementioned set of questions, based on the analysis of gender and personality interactions with regard to film preferences. In an attempt to complement previous research findings, we focused our analysis on the role of sex and personality in film preferences for British Facebook users aged 16-25. We paid attention to their scores on online Big Five personality questionnaires and their stated gender, using both single and multivariate measures to relate the two variables to film preferences, also listed on their profiles. The following hypotheses were tested: 1. Females and males differ with regard to film preferences. 2. Personality characteristics differ according to gender. 3. Film preferences vary in accordance with personality characteristics. 4. There is a significant interaction between gender and personality with regard to film preferences. the explanatory potential of online social networks Online social networks are mass repositories of valuable social and psychological information for researchers. Facebook, ranked as the most popular social network by worldwide monthly users, hosts for instance more than 400 million user profiles, in which individuals’ number of friends, media content preferences and socio-demographic background– amongst other things are displayed. Their systematic use for data collection purposes is a relatively recent trend in social psychological research; Stillwell’s creation of the myPersonality application (2007) on Facebook is a striking exemplar of the considerable explanatory potential of those online social networks. With myPersonality, users can take an online version of the Big Five personality questionnaire and agree to share their profile information for research purposes. As such, users’ personalities can be matched to any other single or collection of individual information available on Facebook, opening up an array of possible areas of investigation. GENDER, PERSONALITY AND FILM PREFERENCES 4 the predominance of visual media The appeal of visual media is easily demonstrated in terms of the importance it has in the lives of most individuals. Data from the National Survey of Culture, Leisure and Sport (2007) indicates that watching television is the most common leisure activity for over eight out of ten men and women in England, taking precedence over spending time with friends and family, sport, etc. Another intriguing finding relates to TV and movie watching online: the number of Britons visiting such websites reached 21 million in 2007. Although it is clear that many people enjoy visual media offerings, it is much more difficult to pinpoint the specific viewer characteristics that predict genre enjoyment. In other words, who prefers which type of content? visual media content: an explanation of viewer preferences Initially the domain of inquiry of communication theorists, media preferences and their determinants are increasingly studied from the vantage point of a variety of disciplines, amongst which psychology and sociology feature predominantly (Kraaykamp et al, 2005). Their combined explanatory potential finds its articulation in the uses and gratification approach (Blumler & Katz, 1974; Rosengreen & Windhal, 1989), according to which preferences for specific media and/or specific content result from an individual’s psychological and social attributes. gender differences in media content preferences Research on media preferences has paid notable attention to sex differences in responses to different types of film. It has been established that various movie genres elicit differentiated affective responses between the two sexes. Rather interestingly, the widespread gender – stereotyping running through entertainment products seems to correspond to actual viewer preferences. Males indeed prefer action/adventure genres typically associated with the masculine sex, whilst females have a soft spot for romance/drama. Oliver et al. (2000) point to the explanatory potential of differential gender-linked expectations, which can be conceptualized along two dimensions: (1) communality, typically associated with females and (2) agency, often associated with males (Eagly, 1987). Depending on whether they are communal or agency-consistent, the themes preponderant in each genre make up for a sexually differentiated public. A number of studies highlighted the importance of sex in social affiliation with media characters: viewers tend to affiliate more strongly with and GENDER, PERSONALITY AND FILM PREFERENCES 5 experience more intense emotional responses to same-sex characters (e.g. Deutsch, 1975; Hoffner & Cantor, 1991). Another potential explanation is that of sex differences in mean personality levels, which remain fairly constant across the lifespan (Feingold, 1994; McCrae & Costa, 1984). personality differences in media preferences Theorists have also focused considerable attention on the role of personality characteristics in modern mass communication theory (Blumler & Katz, 1974; Wober, 1986). In most personality/media use models, personality is conceptualized as the nexus of attitudes, beliefs and values that guide one’s cognitive and affective interactions with the social environment (Weaver et al, 1993). Personality characteristics are thus believed to influence media preferences, which are essentially evaluative judgements pertaining to the gratifications consumers anticipate from their interaction with the media (Palmgreen, 1984). The Big Five framework of personality is most frequently employed by researchers seeking to demonstrate empirical connections between media gratifications and their psychological roots (e.g. Kraaykamp, 2001; Kraaykamp et al, 2005). It includes the following domains: Openness (traits like originality and open-mindedness), Conscientiousness (traits like hardworking and orderly), Extraversion (traits like energetic and sociable), Agreeableness (traits like affectionate and considerate) and Neuroticism (traits like nervous and tense) (Donellan & Lucas, 2008). With regard to media preferences, individuals scoring high on the dimension of Openness seem to prefer original and serious media content, whilst conscientious individuals like predictable and structured formats (Kraaykamp et al, 2005). The findings for extroverts are less straightforward: they seemingly favour sensory stimulation because of relatively high arousal levels (Costa & McCrae, 1988). Agreeableness appears to be the second most important personality trait, after Openness, in predicting visual preferences. Friendly individuals tend to watch popular content, devoid of upsetting and unconventional images (Kraykaamp, 2001). Finally, emotionally unstable individuals are thought to find easy means of escape from feelings of tension and stress in watching TV (Conway & Rubin, 1991). Most studies on personality effects in media preferences tend to either overlook or control for participants’ gender (e.g. Weaver et al, 1993; Kraaykamp, 2001). As a result, it is difficult to tell in the former case whether the differentiated association between media GENDER, PERSONALITY AND FILM PREFERENCES 6 preferences and personality characteristics is partially caused by the fact that personality traits differ systematically between people of different sexes (Kraaykamp & Van Eijck, 2005). The latter design precludes the possibility of assessing the interaction between gender and personality when it comes to predicting media taste. It seemed to us as though no one has investigated gendered differences in personality in the context of similar genre preferences.

Crowd employment is a new form of short term and flexible employment which has emerged during the past decade. For understanding this new form of employment, it is crucial to understand the underlying motivations of the workforce involved in it. This paper presents the Multidimensional Crowdworker Motivation Scale (MCMS), a scale for measuring the motivation of crowdworkers on micro-task platforms. The scale is theoretically grounded in Self-Determination Theory and tailored specifically to the context of crowdsourced micro-labor. The MCMS was validated on data collected in ten countries and three income groups. Furthermore, measurement invariance tests showed that motivations measured with the MCMS are comparable across countries and income groups. This work constitutes a first step towards understanding the motivations of the international crowd workforce.

Cyberloafing is the personal use of the Internet by employees while at work. The purpose of this study is to examine whether employee job attitudes, organizational characteristics, attitudes towards cyberloafing, and other non-Internet loafing behaviors serve as antecedents to cyberloafing behaviors. We hypothesize that the employee job attitudes of job involvement and intrinsic involvement are related to cyberloafing. In addition, we hypothesize that organizational characteristics including the perceived cyberloafing of one’s coworkers and managerial support for internet usage are related to cyberloafing. We also hypothesize that attitudes towards cyberloafing and the extent to which employees participate in non-Internet loafing behaviors (e.g., talking with coworkers, running personal errands) will both be related to cyberloafing. One hundred and forty-three working professional from a variety of industries were surveyed regarding their Internet usage at work. As hypothesized, the employee job attitudes of job involvement and intrinsic involvement were negatively related to cyberloafing. Also as predicted, the organizational characteristics of the perceived cyberloafing of one’s coworkers and managerial support for internet usage were positively related to cyberloafing. Finally, results showed that attitudes towards cyberloafing and participation in non-Internet loafing behaviors were positively related to cyberloafing. Implications for both organizations and employees are discussed. 2011 Elsevier Ltd. All rights reserved.

A growing body of research suggests that personality characteristics relate to physical health; however, this relation ship has primarily been tested in cross-sectional studies that have not followed the participants into old age. The present study utilizes data from a 70-year longitudinal study to prospectively examine the relationship between the adaptive defense mechanisms in midlife and objectively assessed physical health in late life. In addition to examining the direct effect, we test whether social support mediates this relation ship. The sample consisted of 90 men who were followed for over seven decades beginning in late adolescence. Health ratings from medical records were made at three time points (ages 70, 75, and 80). Defense mechanisms were coded from narratives by trained independent raters (Vaillant, Bond, & Vaillant, 1986). Independent raters assessed social supports between ages 50 and 70. More adaptive defenses in midlife were associated with better physical health at all three time points in late life. These relationships were partially mediated by social support. Findings are consistent with the theory that defense maturity is important for building social relationships, which in turn contribute to better late-life physical health. Psychological interventions aimed at improving these domains may be beneficial for physical health.


Question: The correlation between personality traits and counterproductive work behaviors: examining the mediating impact of job satisfaction.

This paper re-examines the relationship between the Big Five personality traits and job performance, addressing previous methodological limitations. Prior research has been criticized for including data not derived from genuine Big Five assessments, potentially compromising construct validity. Furthermore, earlier studies neglected the correlation between the Big Five and contextual performance. To rectify these issues, this study conducts a meta-analysis focusing on the criterion-related validity of the Big Five measures in predicting both job performance and contextual performance. The findings for job performance align closely with two prior meta-analyses, but the analysis of contextual performance reveals more nuanced relationships between the Big Five and performance outcomes. The paper offers a more rigorous interpretation of the Big Five-performance nexus and outlines directions for future research aimed at bolstering the predictive validity of personality traits in the workplace.

The investigation of personality traits and their impact on job performance has been a subject of significant interest in organizational psychology. However, previous meta-analyses exploring the relationship between the Big Five personality dimensions and job performance have faced criticism for potential construct validity issues, stemming from the inclusion of data not collected using authentic Big Five instruments. Moreover, these studies have failed to explore the connections between the Big Five and contextual performance, which encompasses behaviors that contribute to the broader organizational environment. To address these gaps, the present study conducts a meta-analysis that specifically evaluates the predictive validity of the Big Five measures for both job performance and contextual performance. The outcomes for job performance largely echo the findings of two previous meta-analyses, while the contextual performance results uncover more intricate patterns linking the Big Five to performance. This study provides a more thorough understanding of the Big Five-performance relationship and suggests avenues for future research to enhance the effectiveness of personality assessments in predicting job-related behaviors.

BOOK REVIEW: Csikszentmihalyi, M. (2008). Flow: The Psychology of Optimal Experience. New York, NY: HarperCollins. 336 pp. ISBN 978-0-06-133920-2.

Previous research on the Dark Triad traits (i.e., Machiavellianism, psychopathy, and narcissism) has focused solely on the role the Dark Triad traits played in mate choice of actors. The current study (N = 336) extends this by manipulating the apparent levels of Dark Triad traits in targets and correlating mate choice in these targets with individual differences in the Dark Triad traits in actors. As expected, both sexes preferred partners low in the Dark Triad traits for long-term mating, while those high in these traits were preferred for one-night stands. However, women high in psychopathy considered the Dark Triad traits in potential male partners more physically attractive and desirable for an one-night stand, as well as a potential husband. Men who were high on psychopathy were likewise attracted to psychopathy in potential mothers. Our findings are discussed from an evolutionary personality paradigm. 2015 Elsevier Ltd. All rights reserved.

Seven experts on personality measurement here discuss the viability of public-domain personality measures, focusing on the International Personality Item Pool (IPIP) as a prototype. Since its inception in 1996, the use of items and scales from the IPIP has increased dramatically. Items from the IPIP have been translated from English into more than 25 other languages. Currently over 80 publications using IPIP scales are listed at the IPIP Web site (http://ipip.ori.org), and the rate of IPIPrelated publications has been increasing rapidly. The growing popularity of the IPIP can be attributed to Wve factors: (1) It is cost free; (2) its items can be obtained instantaneously via the Internet; (3) it includes over 2000 items, all easily available for inspection; (4) scoring keys for IPIP scales are This article represents a synthesis of contributions to the presidential symposium, The International Personality Item Pool and the Future of Public-Domain Personality Measures (L.R. Goldberg, Chair) at the sixth annual meeting of the Association for Research in Personality, New Orleans, January 20, 2005. Authorship order is based on the order of participation in the symposium. The IPIP project has been continually supported by Grant MH049227 from the National Institute of Mental Health, U.S. Public Health Service. J.A. Johnson’s research was supported by the DuBois Educational Foundation. The authors thank Paul T. Costa Jr., Samuel D. Gosling, Leonard G. Rorer, Richard Reed, and Krista Trobst for their helpful suggestions. * Corresponding author. Fax: +1 814 375 4784. E-mail address: j5j@psu.edu (J.A. Johnson). 0092-6566/$ see front matter  2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jrp.2005.08.007 L.R. Goldberg et al. / Journal of Research in Personality 40 (2006) 84–96 85 provided; and (5) its items can be presented in any order, interspersed with other items, reworded, translated into other languages, and administered on the World Wide Web without asking permission of anyone. The unrestricted availability of the IPIP raises concerns about possible misuse by unqualiWed persons, and the freedom of researchers to use the IPIP in idiosyncratic ways raises the possibility of fragmentation rather than scientiWc uniWcation in personality research.  2005 Elsevier Inc. All rights reserved.

We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.

BACKGROUND
Serotonin function has been implicated in both major depressive disorder and neuroticism. In the current investigation, we examined the hypothesis that any change in depression severity is mediated through the reduction of neuroticism, but only for those compounds which target serotonin receptors.

METHODS
Ninety-three outpatients in the midst of a major depressive episode received one of three antidepressant medications, classified into two broad types: selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs (i.e. reversible monoamine oxidase inhibitors [RIMAs] and noradrenergic and dopaminergic reuptake blockers [NDMs]). Patients completed the Hamilton Rating Scale for Depression, Beck Depression Inventory II and Revised NEO Personality Inventory prior to and following approximately 16 weeks of treatment. Structural equation modeling was used to test two models: a mediation model, in which neuroticism change is the mechanism by which SSRIs exert a therapeutic effect upon depressive symptoms, and a complication model, in which neuroticism change is a mere epiphenomenon of depression reduction in response to SSRIs.

RESULTS
The mediation model provided a good fit to the data; the complication model did not. Patients treated with SSRIs demonstrated greater neuroticism change than those treated with non-SSRIs, and greater neuroticism change was associated with greater depressive symptom change. These effects held for both self-reported and clinician-rated depressive symptom severity.

LIMITATIONS
Replication within a randomized control trial with multiple assessment periods is required.

CONCLUSION
Neuroticism mediates changes in depression in response to treatment with SSRIs, such that any treatment effect of SSRIs occurs through neuroticism reduction.

A conditional approach to dispositions is developed in which dispositional constructs are viewed as clusters of if-then propositions. These propositions summarize contingencies between categories of conditions and categories of behavior rather than generalized response tendencies. A fundamental unit for investigating dispositions is therefore the conditional frequency of acts that are central to a given behavior category in circumscribed situations, not the overall frequency of behaviors. In an empirical application of the model, we examine how people's dispositional judgments are linked to extensive observations of targets' behavior in a range of natural social situations. We identify categories of these social situations in which targets' behavior may be best predicted from observers' dispositional judgements, focusing on the domains of aggression and withdrawal. One such category consists of subjectively demanding or stressful situations that tax people's performance competencies. As expected, children judged to be aggressive or withdrawn were variable across situations in dispositionally relevant behaviors, but they diverged into relatively predictable aggressive and withdrawn actions in situations that required the social, self-regulatory, and cognitive competencies they lacked. Implications of the conditional approach for personality assessment and person perception research are considered.

Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.

Narcissists are thought to display extreme affective reactions to positive and negative information about the self. Two experiments were conducted in which high- and low-narcissistic individuals, as defined by the Narcissistic Personality Inventory (NPI), completed a series of tasks in which they both succeeded and failed. After each task, participants made attributions for their performance and reported their moods. High-NPI participants responded with greater changes in anxiety, anger, and self-esteem. Low self-complexity was examined, but it neither mediated nor moderated affective responses. High-NPI participants tended to attribute initial success to ability, leading to more extreme anger responses and greater self-esteem reactivity to failure. A temporal sequence model linking self-attribution and emotion to narcissistic rage is discussed.


Question: The connection between personality characteristics and counterproductive workplace actions: the mediating impact of job contentment.

The Influence of Methodological Biases on Behavioral Research: A Critical Review and Recommendations for Mitigation

The issue of methodological biases has been a persistent concern in the behavioral sciences, with a rich history of scholarly debate. However, there is a notable absence of a thorough, up-to-date summary that addresses the potential origins of these biases and the strategies for their control. This article aims to fill this gap by critically examining the impact of method biases on the outcomes of behavioral research. It identifies the various sources of method biases, delves into the cognitive mechanisms through which these biases manifest in responses to measurement tools, and evaluates the effectiveness of diverse procedural and statistical methods for mitigating these biases.

Moreover, the article provides practical guidance for researchers in selecting the most appropriate procedural and statistical approaches to counteract method biases, tailored to the specific contexts of their studies. By offering a comprehensive review and actionable recommendations, this paper seeks to enhance the validity and reliability of behavioral research findings, particularly in relation to personality traits and counterproductive work behaviors, by addressing the mediating effects of job satisfaction.

In the context of the social network service environment, we explore how discrete emotions—envy and shame, in particular—may mediate the effects of social comparison on behavior intention and psychological responses. Based on the survey responses of 446 university students, the results suggest that social comparison to media figures correlates with a range of emotional responses as well as with behavioral intention and psychological responses. Envy maintained a significantly greater association with switch intention as a behavioral intention compared to shame. Conversely, shame was significantly related to burnout as a psychological response. Further, mediational analyses were consistent with the argument that envy and shame mediate the social comparison–outcome variables relationship. This research helps to illuminate the driving mechanism for the emotional effect that social comparison on social network service could elicit from a user. This predicts the nature of the behavioral and psychological outcome associated with the comparison and has implications for an enhanced understanding of the way in which the unique social network service communication environment may stimulate this process. 2015 Elsevier Ltd. All rights reserved.

Most research to date has approached employee empowerment as an individual-level phenomenon. In this study we proposed a work-unit-level construct, empowerment climate, and tested a multiple-level model integrating macro and micro approaches to empowerment. Empowerment climate was shown to be empirically distinct from psychological empowerment and positively related to manager ratings of work-unit performance. A cross-level mediation analysis using hierarchical linear modeling showed that psychological empowerment mediated the relationships between empowerment climate and individual performance and job satisfaction.

Gaps in research focusing on work related stress, burnout, job satisfaction and general health of nurses is evident within developing contexts like South Africa. This study identified the relationship between work related stress, burnout, job satisfaction and general health of nurses. A total of 1200 nurses from four hospitals were invited to participate in this cross-sectional study (75% response rate). Participants completed five questionnaires and multiple linear regression analysis was used to determine significant relationships between variables. Staff issues are best associated with burnout as well as job satisfaction. Burnout explained the highest amount of variance in mental health of nurses. These are known to compromise productivity and performance, as well as affect the quality of patient care. Issues, such as security risks in the workplace, affect job satisfaction and health of nurses. Although this is more salient to developing contexts it is important in developing strategies and intervention programs towards improving nurse and patient related outcomes.

Gray (1981, 1982) holds that 2 general motivational systems underlie behavior and affect: a behavioral inhibition system (BIS) and a behavioral activation system (BAS). Self-report scales to assess dispositional BIS and BAS sensitivities were created. Scale development (Study 1) and convergent and discriminant validity in the form of correlations with alternative measures are reported (Study 2). In Study 3, a situation in which Ss anticipated a punishment was created. Controlling for initial nervousness, Ss high in BIS sensitivity (assessed earlier) were more nervous than those low. In Study 4, a situation in which Ss anticipated a reward was created. Controlling for initial happiness, Ss high in BAS sensitivity (Reward Responsiveness and Drive scales) were happier than those low. In each case the new scales predicted better than an alternative measure. Discussion is focused on conceptual implications.

The present study employed multivariate genetic item-level analyses to examine the ontology and the genetic and environmental etiology of the Big Five personality dimensions, as measured by the NEO Five Factor Inventory (NEO-FFI) [Costa and McCrae, Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO-FFI) professional manual, 1992; Hoekstra et al., NEO personality questionnaires NEO-PI-R, NEO-FFI: manual, 1996]. Common and independent pathway model comparison was used to test whether the five personality dimensions fully mediate the genetic and environmental effects on the items, as would be expected under the realist interpretation of the Big Five. In addition, the dimensionalities of the latent genetic and environmental structures were examined. Item scores of a population-based sample of 7,900 adult twins (including 2,805 complete twin pairs; 1,528 MZ and 1,277 DZ) on the Dutch version of the NEO-FFI were analyzed. Although both the genetic and the environmental covariance components display a 5-factor structure, applications of common and independent pathway modeling showed that they do not comply with the collinearity constraints entailed in the common pathway model. Implications for the substantive interpretation of the Big Five are discussed.

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.


Question: The connection between personality characteristics and counterproductive work behaviors: The mediating impact of job satisfaction.

This comprehensive meta-analysis explores the relationship between 137 distinct personality traits and subjective well-being (SWB), employing a rigorous literature search encompassing multiple strategies. The findings reveal that personality traits are similarly influential in predicting life satisfaction, happiness, and positive affect, but exhibit a lesser predictive power for negative affect. Among the traits examined, repressive-defensiveness, trust, emotional stability, locus of control-chance, desire for control, hardiness, positive affectivity, private collective self-esteem, and tension stood out as the most strongly associated with SWB. When these traits were categorized under the Big Five personality factors, Neuroticism emerged as the dominant predictor for life satisfaction, happiness, and negative affect. Both Extraversion and Agreeableness were found to be equally potent predictors of positive affect. The analysis delves into the significance of personality in forecasting SWB, proposes potential mechanisms through which personality impacts SWB, and acknowledges the constraints of the current review.

This study was conducted to answer this simple question: ‘Can cultural values explain global social media use?’ Along with cultural dimensions introduced by past studies we also added several demographic, socio-economic and personality variables into this study that generated quite interesting findings. We found that there are low levels of suicide, more happiness and more corruption in societies that use social media heavily. We also observed that GDP per capita and median age are negatively related with social media use. Self-esteem stood out as important variable related to social media use intensity along with emotional expressiveness, openness and conscientiousness. Contrary to the common view, nation-level social capital achievement was negatively related with social media use and there was absolutely no relationship between high-context and low-context communication characteristics and local social media use. Some other findings also indicated that conservative and collectivistic countries use social media more often than do individualistic and developed countries. Schwartz’s cultural dimensions and the results of the GLOBE study accounted for a considerable amount of variation in country-level social media use where Hofstede and Trompenaars’ cultural dimensions were insignificant. Since most of the cultural values failed to explain the intensity of social media use, we also developed a cross-cultural online communication framework called cross-cultural self and others’ worth.

Incorporating users’ personality traits has shown to be instrumental in many personalized retrieval and recommender systems. Analysis of users’ digital traces has become an important resource for inferring personality traits. To date, the analysis of users’ explicit and latent characteristics is typically restricted to a single social networking site (SNS). In this work, we propose a novel method that integrates text, image, and users’ meta features from two different SNSs: Twitter and Instagram. Our preliminary results indicate that the joint analysis of users’ simultaneous activities in two popular SNSs seems to lead to a consistent decrease of the prediction errors for each personality trait.

BACKGROUND
To investigate the prevalence of Internet addiction among Chinese adolescents and to explore the psychological features associated with Internet addiction.

METHODS
A total of 2620 high school students from four high schools in Changsha City were surveyed using Diagnostic Questionnaire for Internet Addiction (YDQ), Eysenck Personality Questionnaire (the edition for children, EPQ), Time Management Disposition Scale (TMDS) and Strengths and Difficulties Questionnaire (SDQ). The mean age of whole sample was 15.19 years (ranging from 12 years to 18 years). According to the modified YDQ criteria by Beard, 64 students who were diagnosed as Internet addiction (the mean age: 14.59 years) and 64 who were diagnosed as being normal in Internet usage (the mean age: 14.81 years) were included in a case-control study.

RESULTS
The rate of Internet use among the surveyed adolescents was 88%, among which the incidence rate of Internet addiction was 2.4%. The Internet addiction group had significantly higher scores on the EPQ subscales of neuroticism, psychoticism, and lie than the control group (P < 0.05). The Internet addiction group scored lower than the control group on the TMDS subscales of sense of control over time, sense of value of time, and sense of time efficacy (P < 0.05). Compared with the control group, the Internet addiction group had also significantly higher scores on the SDQ subscales of emotional symptoms, conduct problems, hyperactivity, total difficulties and lower scores on the subscale of prosocial behaviours (P < 0.05).

CONCLUSIONS
The present study suggests that Internet addiction is not rare among Chinese adolescents. In addition, adolescents with Internet addiction possess different psychological features when compared with those who use the Internet less frequently.

Facebook is quickly becoming one of the most popular tools for social communication. However, Facebook is somewhat different from other Social Networking Sites as it demonstrates an offline-to-online trend; that is, the majority of Facebook Friends are met offline and then added later. The present research investigated how the Five-Factor Model of personality relates to Facebook use. Despite some expected trends regarding Extraversion and Openness to Experience, results indicated that personality factors were not as influential as previous literature would suggest. The results also indicated that a motivation to communicate was influential in terms of Facebook use. It is suggested that different motivations may be influential in the decision to use tools such as Facebook, especially when individual functions of Facebook are being considered. Ó 2008 Elsevier Ltd. All rights reserved. 1. Personality correlates and competency factors associated


Question: Integrating syntactic uncertainty into neural machine translation using a forest-to-sequence model.

Enhanced Neural Machine Translation through Syntax Integration with a Bidirectional Tree Encoder and Tree-Coverage Mechanism

Recent advancements in neural machine translation (NMT) have primarily focused on sequential encoder-decoder frameworks, neglecting the potential benefits of syntactic information. This paper presents an innovative approach to address this gap by explicitly integrating source-side syntactic trees into the NMT model. We introduce two key enhancements: (1) a bidirectional tree encoder that captures both sequential and hierarchical representations of the input; and (2) a tree-coverage mechanism that modifies the attention mechanism to account for source-side syntax. Our experimental evaluation on Chinese-English translation tasks reveals that these enhancements significantly outperform conventional sequential attentional models and a baseline incorporating a bottom-up tree encoder and word coverage. This demonstrates the effectiveness of our syntax-aware approach in improving NMT accuracy.

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.

We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser actions are determined by a classifier, based on features that represent the current state of the parser. We apply this parsing framework to both tracks of the CoNLL 2007 shared task, in each case taking advantage of multiple models trained with different learners. In the multilingual track, we train three LR models for each of the ten languages, and combine the analyses obtained with each individual model with a maximum spanning tree voting scheme. In the domain adaptation track, we use two models to parse unlabeled data in the target domain to supplement the labeled out-ofdomain training set, in a scheme similar to one iteration of co-training.

Universal Dependencies is a recent initiative to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. In this talk, I outline the motivation behind the initiative and explain how the basic design principles follow from these requirements. I then discuss the different components of the annotation standard, including principles for word segmentation, morphological annotation, and syntactic annotation. I conclude with some thoughts on the challenges that lie ahead.

Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.

We present the first user study on neural interactive translation prediction. Our neural translation aid was built into existing online translation software. Depending on the prefix already typed by the user the system suggests a list of continuations. We assessed the impact of the system on human translators in a user study with both professional and non-professional translators. The analysis was done with respect to translation speed and translation accuracy as assessed by human judges. We find that our neural translation aid enables relatively fast translations and does not compromise on quality.

Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate.

Recent progress in deep generative models has been fueled by two paradigms – autoregressive and adversarial models. We propose a combination of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.

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.

Arc-eager dependency parsers process sentences in a single left-to-right pass over the input and have linear time complexity with greedy decoding or beam search. We show how such parsers can be constrained to respect two different types of conditions on the output dependency graph: span constraints, which require certain spans to correspond to subtrees of the graph, and arc constraints, which require certain arcs to be present in the graph. The constraints are incorporated into the arc-eager transition system as a set of preconditions for each transition and preserve the linear time complexity of the parser.

The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.


Question: Integrating syntactic uncertainty into neural machine translation using a forest-to-sequence model.

This paper presents an enhanced approach to sequence modeling tasks, particularly in the context of natural language processing, through the utilization of Tree-Structured Long Short-Term Memory (Tree-LSTM) networks. Building upon the success of LSTMs, which are recurrent neural networks capable of effectively retaining sequence information over extended periods, the Tree-LSTM architecture introduces a significant advancement by adopting a tree-structured network topology. Unlike the conventional linear chain structure of LSTMs, the Tree-LSTM's design aligns more closely with the syntactic properties inherent in natural language, enabling the network to naturally group words into meaningful phrases. This innovative approach yields superior performance compared to existing systems and robust LSTM baselines in two key areas: assessing the semantic relatedness of two sentences, as demonstrated in the SemEval 2014 Task 1, and sentiment classification, evidenced by its application in the Stanford Sentiment Treebank. The Tree-LSTM's ability to incorporate syntactic structure into its computations showcases its potential for improving semantic representations in neural machine translation and other language processing tasks.

Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Earlier attempts to grammatical error correction involve rule-based and classifier approaches which are limited to correcting only some particular type of errors in a sentence. As sentences may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this report, we investigate GEC as a translation task from incorrect to correct English and explore some machine translation approaches for developing end-to-end GEC systems for all error types. We apply Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) approaches to GEC and show that they can correct multiple errors of different types in a sentence when compared to the earlier methods which focus on individual errors. We also discuss some of the weakness of machine translation approaches. Finally, we also experiment on a candidate re-ranking technique to re-rank the hypotheses generated by machine translation systems. With regression models, we try to predict a grammaticality score for each candidate hypothesis and re-rank them according to the score.

Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.

Universal Dependencies is a recent initiative to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. In this talk, I outline the motivation behind the initiative and explain how the basic design principles follow from these requirements. I then discuss the different components of the annotation standard, including principles for word segmentation, morphological annotation, and syntactic annotation. I conclude with some thoughts on the challenges that lie ahead.

Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual similarity and/or syntactic role. The findings suggest that, while monolingual models learn information about how concepts are related, neural-translation models better capture their true ontological status. It is well known that word representations can be learned from the distributional patterns in corpora. Originally, such representations were constructed by counting word co-occurrences, so that the features in one word’s representation corresponded to other words [11, 17]. Neural language models, an alternative means to learn word representations, use language data to optimise (latent) features with respect to a language modelling objective. The objective can be to predict either the next word given the initial words of a sentence [4, 14, 8], or simply a nearby word given a single cue word [13, 15]. The representations learned by neural models (sometimes called embeddings) generally outperform those acquired by co-occurrence counting models when applied to NLP tasks [3]. Despite these clear results, it is not well understood how the architecture of neural models affects the information encoded in their embeddings. Here, we explore this question by considering the embeddings learned by architectures with a very different objective function to monolingual language models: neural machine translation models. We show that translation-based embeddings outperform monolingual embeddings on two types of task: those that require knowledge of conceptual similarity (rather than simply association or relatedness), and those that require knowledge of syntactic role. We discuss what the findings indicate about the information content of different embeddings, and suggest how this content might emerge as a consequence of the translation objective. 1 Learning embeddings from language data Both neural language models and translation models learn real-valued embeddings (of specified dimension) for words in some pre-specified vocabulary, V , covering many or all words in their training corpus. At each training step, a ‘score’ for the current training example (or batch) is computed based on the embeddings in their current state. This score is compared to the model’s objective function, and the error is backpropagated to update both the model weights (affecting how the score is computed from the embeddings) and the embedding features. At the end of this process, the embeddings should encode information that enables the model to optimally satisfy its objective. 1.1 Monolingual models In the original neural language model [4] and subsequent variants [8], each training example consists of n subsequent words, of which the model is trained to predict the n-th word given the first n − 1 ar X iv :1 41 0. 07 18 v2 [ cs .C L ] 1 3 N ov 2 01 4 1 words. The model first represents the input as an ordered sequence of embeddings, which it transforms into a single fixed length ‘hidden’ representation by, e.g., concatenation and non-linear projection. Based on this representation, a probability distribution is computed over the vocabulary, from which the model can sample a guess at the next word. The model weights and embeddings are updated to maximise the probability of correct guesses for all sentences in the training corpus. More recent work has shown that high quality word embeddings can be learned via models with no nonlinear hidden layer [13, 15]. Given a single word in the corpus, these models simply predict which other words will occur nearby. For each word w in V , a list of training cases (w, c) : c ∈ V is extracted from the training corpus. For instance, in the skipgram approach [13], for each ‘cue word’ w the ‘context words’ c are sampled from windows either side of tokens of w in the corpus (with c more likely to be sampled if it occurs closer to w).1 For each w in V , the model initialises both a cue-embedding, representing the w when it occurs as a cue-word, and a context-embedding, used when w occurs as a context-word. For a cue word w, the model can use the corresponding cueembedding and all context-embeddings to compute a probability distribution over V that reflects the probability of a word occurring in the context of w. When a training example (w, c) is observed, the model updates both the cue-word embedding of w and the context-word embeddings in order to increase the conditional probability of c. 1.2 Translation-based embeddings Neural translation models generate an appropriate sentence in their target language St given a sentence Ss in their source language [see, e.g., 16, 6]. In doing so, they learn distinct sets of embeddings for the vocabularies Vs and Vt in the source and target languages respectively. Observing a training case (Ss, St), such a model represents Ss as an ordered sequence of embeddings of words from Vs. The sequence for Ss is then encoded into a single representation RS .2 Finally, by referencing the embeddings in Vt, RS and a representation of what has been generated thus far, the model decodes a sentence in the target language word by word. If at any stage the decoded word does not match the corresponding word in the training target St, the error is recorded. The weights and embeddings in the model, which together parameterise the encoding and decoding process, are updated based on the accumulated error once the sentence decoding is complete. Although neural translation models can differ in low-level architecture [7, 2], the translation objective exerts similar pressure on the embeddings in all cases. The source language embeddings must be such that the model can combine them to form single representations for ordered sequences of multiple words (which in turn must enable the decoding process). The target language embeddings must facilitate the process of decoding these representations into correct target-language sentences. 2 Comparing Mono-lingual and Translation-based Embeddings To learn translation-based embeddings, we trained both the RNN encoder-decoder [RNNenc, 7] and the RNN Search architectures [2] on a 300m word corpus of English-French sentence pairs. We conducted all experiments with the resulting (English) source embeddings from these models. For comparison, we trained a monolingual skipgram model [13] and its Glove variant [15] for the same number of epochs on the English half of the bilingual corpus. We also extracted embeddings from a full-sentence language model [CW, 8] trained for several months on a larger 1bn word corpus. As in previous studies [1, 5, 3], we evaluate embeddings by calculating pairwise (cosine) distances and correlating these distances with (gold-standard) human judgements. Table 1 shows the correlations of different model embeddings with three such gold-standard resources, WordSim-353 [1], MEN [5] and SimLex-999 [10]. Interestingly, translation embeddings perform best on SimLex-999, while the two sets of monolingual embeddings perform better on modelling the MEN and WordSim353. To interpret these results, it should be noted that SimLex-999 evaluation quantifies conceptual similarity (dog wolf ), whereas MEN and WordSim-353 (despite its name) quantify more general relatedness (dog collar) [10]. The results seem to indicate that translation-based embeddings better capture similarity, while monolingual embeddings better capture relatedness. 1 Subsequent variants use different algorithms for selecting the (w, c) from the training corpus [9, 12] Alternatively, subsequences (phrases) of Ss may be encoded at this stage in place of the whole sentence [2].

We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.1

Previous phrase-based approaches to Automatic Post-editing (APE) have shown that the dependency of MT errors from the source sentence can be exploited by jointly learning from source and target information. By integrating this notion in a neural approach to the problem, we present the multi-source neural machine translation (NMT) system submitted by FBK to the WMT 2017 APE shared task. Our system implements multi-source NMT in a weighted ensemble of 8 models. The n-best hypotheses produced by this ensemble are further re-ranked using features based on the edit distance between the original MT output and each APE hypothesis, as well as other statistical models (n-gram language model and operation sequence model). This solution resulted in the best system submission for this round of the APE shared task for both en-de and de-en language directions. For the former language direction, our primary submission improves over the MT baseline up to -4.9 TER and +7.6 BLEU points. For the latter, where the higher quality of the original MT output reduces the room for improvement, the gains are lower but still significant (-0.25 TER and +0.3 BLEU).

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.

Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs, using back-propagation through time, often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach relative to stochastic optimization.

We present a new approach to evaluate computational models for the task of text understanding by the means of out-of-context error detection. Through the novel design of our automated modification process, existing large-scale data sources can be adopted for a vast number of text understanding tasks. The data is thereby altered on a semantic level, allowing models to be tested against a challenging set of modified text passages that require to comprise a broader narrative discourse. Our newly introduced task targets actual real-world problems of transcription and translation systems by inserting authentic out-of-context errors. The automated modification process is applied to the 2016 TEDTalk corpus. Entirely automating the process allows the adoption of complete datasets at low cost, facilitating supervised learning procedures and deeper networks to be trained and tested. To evaluate the quality of the modification algorithm a language model and a supervised binary classification model are trained and tested on the altered dataset. A human baseline evaluation is examined to compare the results with human performance. The outcome of the evaluation task indicates the difficulty to detect semantic errors for machine-learning algorithms and humans, showing that the errors cannot be identified when limited to a single sentence.

The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the benefits of the dependencybased convolutional neural networks and the entity mentionbased pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions.


Question: Integrating syntactic uncertainty into neural machine translation using a forest-to-sequence model.

This paper presents an enhanced approach to integrate structural alignment biases into an attentional neural machine translation framework. Neural encoder-decoder models have proven highly effective in machine translation, often matching the performance of conventional models. Nevertheless, these models often lack the sophisticated inductive biases found in traditional systems. To address this, we have augmented the attentional neural translation model with structural biases derived from word-based alignment models. These biases encompass positional influence, Markov conditioning, fertility considerations, and agreement on translation directions. Through rigorous testing, we have observed significant enhancements over a baseline attentional model and a standard phrase-based model across various language pairs. Our evaluation, conducted in a low-resource setting with challenging languages, underscores the effectiveness of our approach in improving translation quality by leveraging alignment biases.

We propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be refined (subcategorized) to fit the training data. We aim to provide a unified model where TSG rules and symbol refinement are learned from training data in a fully automatic and consistent fashion. We present a novel probabilistic SR-TSG model based on the hierarchical Pitman-Yor Process to encode backoff smoothing from a fine-grained SR-TSG to simpler CFG rules, and develop an efficient training method based on Markov Chain Monte Carlo (MCMC) sampling. Our SR-TSG parser achieves an F1 score of 92.4% in the Wall Street Journal (WSJ) English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and better than state-of-the-art discriminative reranking parsers.

Although NeuroCRF, an augmented Conditional Random Fields (CRF) model whose feature function is parameterized as a Feed-Forward Neural Network (FF NN) on word embeddings, has soundly outperformed traditional linear-chain CRF on many sequence labeling tasks, it is held back by the fact that FF NNs have a fixed input length and therefore cannot take advantage of the full input sentence. We propose to address this issue by replacing the FF NN with a Long Short-Term Memory (LSTM) NN, which can summarize an input of arbitrary length into a fixed dimension representation. The resulting model obtains F1=89.28 on WikiNER dataset, a significant improvement over the NeuroCRF baseline’s F1=87.58, which is already a highly competitive result.

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.

Punctuation prediction is an important task in Spoken Language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctuation prediction and show improvements in the quality of the final translation output. In our experiments we compare the different approaches and show improvements of up to 0.8 BLEU points on the IWSLT 2011 English French Speech Translation of Talks task using a translation system to translate from unpunctuated to punctuated text instead of a language model based punctuation prediction method. Furthermore, we do a system combination of the hypotheses of all our different approaches and get an additional improvement of 0.4 points in BLEU.

We compare the effectiveness of four different syntactic CCG parsers for a semantic slotfilling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation provides a unique window to explore the strengths and weaknesses of semantics captured by unsupervised grammar induction systems. We release a new Freebase semantic parsing dataset called SPADES (Semantic PArsing of DEclarative Sentences) containing 93K cloze-style questions paired with answers. We evaluate all our models on this dataset. Our code and data are available at https://github.com/ sivareddyg/graph-parser.

Previous studies show that incorporating external information could improve the translation quality of Neural Machine Translation (NMT) systems. However, there are inevitably noises in the external information, severely reducing the benefit that the existing methods could receive from the incorporation. To tackle the problem, this study pays special attention to the discrimination of the noises during the incorporation. We argue that there exist two kinds of noise in this external information, i.e. global noise and local noise, which affect the translations for the whole sentence and for some specific words, respectively. Accordingly, we propose a general framework that learns to jointly discriminate both the global and local noises, so that the external information could be better leveraged. Our model is trained on the dataset derived from the original parallel corpus without any external labeled data or annotation. Experimental results in various real-world scenarios, language pairs, and neural architectures indicate that discriminating noises contributes to significant improvements in translation quality by being able to better incorporate the external information, even in very noisy conditions.

Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare that exact search or parameter estimation is tractable. Instead of learning exact models and searching via heuristic means, we embrace this difficulty and treat the structured output problem in terms of approximate search. We present a framework for learning as search optimization, and two parameter updates with convergence the-orems and bounds. Empirical evidence shows that our integrated approach to learning and decoding can outperform exact models at smaller computational cost.

This paper proposes a neural generative model, namely Table2Seq, to generate a natural language sentence based on a table. Specifically, the model maps a table to continuous vectors and then generates a natural language sentence by leveraging the semantics of a table. Since rare words, e.g., entities and values, usually appear in a table, we develop a flexible copying mechanism that selectively replicates contents from the table to the output sequence. We conduct extensive experiments to demonstrate the effectiveness of our Table2Seq model and the utility of the designed copying mechanism. On the WIKIBIO and SIMPLEQUESTIONS datasets, the Table2Seq model improves the state-of-the-art results from 34.70 to 40.26 and from 33.32 to 39.12 in terms of BLEU-4 scores, respectively. Moreover, we construct an open-domain dataset WIKITABLETEXT that includes 13 318 descriptive sentences for 4962 tables. Our Table2Seq model achieves a BLEU-4 score of 38.23 on WIKITABLETEXT outperforming template-based and language model based approaches. Furthermore, through experiments on 1 M table-query pairs from a search engine, our Table2Seq model considering the structured part of a table, i.e., table attributes and table cells, as additional information outperforms a sequence-to-sequence model considering only the sequential part of a table, i.e., table caption.

In many applications such as personal digital assistants, there is a constant need for new domains to increase the system’s coverage of user queries. A conventional approach is to learn a separate model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck for scaling to a large number of domains. In this paper, we introduce a framework that allows us to have a single model that can handle all domains: including unknown domains that may be created in the future as long as they are covered in the master schema. The key idea is to remove the need for distinguishing domains by explicitly predicting the schema of queries. Given permitted schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under the schema. The proposed model achieves competitive and often superior performance over the conventional model trained separately per domain.

Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-sizefits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.


Question: Integrating syntactic uncertainty into neural machine translation using a forest-to-sequence model.

This study pioneers the integration of predicate-argument structure, specifically semantic-role representations, into neural machine translation (NMT) systems, aiming to enhance meaning preservation and generalization. We leverage Graph Convolutional Networks (GCNs) to infuse semantic bias into sentence encoders, marking a significant departure from conventional, syntax-agnostic approaches. By doing so, we observe notable improvements in BLEU scores, outperforming both syntax-aware and linguistic-agnostic models, particularly on the English-to-German translation task. This advancement underscores the potential of semantic representations in boosting the accuracy and robustness of NMT systems.

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser significantly improves upon state-of-the-art results.

In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation and two morphological inflection tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.

We align pairs of English sentences and corresponding Abstract Meaning Representations (AMR), at the token level. Such alignments will be useful for downstream extraction of semantic interpretation and generation rules. Our method involves linearizing AMR structures and performing symmetrized EM training. We obtain 86.5% and 83.1% alignment F score on development and test sets.

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.

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.

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.

A phrase-based statistical machine translation approach the alignment template approach is described. This translation approach allows for general many-to-many relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order from source to target language can be learned explicitly. The model is described using a log-linear modeling approach, which is a generalization of the often used source-channel approach. Thereby, the model is easier to extend than classical statistical machine translation systems. We describe in detail the process for learning phrasal translations, the feature functions used, and the search algorithm. The evaluation of this approach is performed on three different tasks. For the German-English speech Verbmobil task, we analyze the effect of various system components. On the French-English Canadian Hansards task, the alignment template system obtains significantly better results than a single-word-based translation model. In the Chinese-English 2002 National Institute of Standards and Technology (NIST) machine translation evaluation it yields statistically significantly better NIST scores than all competing research and commercial translation systems.

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems.

We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.1

Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT). The use of LSTMs in machine translation has significantly improved the translation performance for longer sentences by being able to capture the context and long range correlations of the sentences in their hidden layers. The attention model based NMT system has become state-of-the-art, performing equal or better than other statistical MT approaches. In this paper, we studied the performance of the attention-model based NMT system on the Indian language pair, Hindi and Bengali. We analysed the types of errors that occur in morphologically rich languages when there is a scarcity of large parallel training corpus. We then carried out certain post-processing heuristic steps to improve the quality of the translated statements and suggest further measures.