Controlling User Perceptions of Linguistic Style: Trainable Generation of Personality Traits

Mairesse, François; Walker, Marilyn A.

doi:10.1162/coli_a_00063

Cited by 151 publications

(124 citation statements)

References 45 publications

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“…Yet it remains an open challenge as to how to imbue an agent with these qualities and how to organize the underlying range of expressive variation. The "Big Five" or "OCEAN" model of personality represents an appealing organizing framework [5,23,22,26]. The model has emerged as a standard in psychology, with research over the last fifty years systematically documenting correlatons between a wide range of behaviors and the Big Five traits (extraversion, neuroticism/emotional stability, agreeableness, conscientiousness, openness to experience) [24,30,33].…”

Section: Introductionmentioning

confidence: 99%

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Neff

Toothman

Bowmani

et al. 2011

Lecture Notes in Computer Science

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Abstract. A key goal in agent research is to be able to generate multimodal characters that can reflect a particular personality. The Big Five model of personality provides a framework for codifying personality variation. This paper reviews findings in the psychology literature to understand how the Big Five trait of emotional stability correlates with changes in verbal and nonverbal behavior. Agent behavior was modified based on these findings and a perceptual study was completed to determine if these changes lead to the controllable perception of emotional stability in virtual agents. The results reveal how language variation and the use of self-adaptors can be used to increase or decrease the perceived emotional stability of an agent. Self-adaptors are movements that often involve self-touch, such as scratching or bending one's fingers backwards in an unnatural brace. These results provide guidance on how agent designers can create particular characters, including indicating that for particular personality types, it is important to also produce typically non-communicative gestural behavior, such as the self-adaptors studied.

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Section: Introductionmentioning

confidence: 99%

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Neff

Toothman

Bowmani

et al. 2011

Lecture Notes in Computer Science

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“…Some works tackling stylistic control of text produced in a rule-based generation system include the works of Power et al (2003); Reiter and Williams (2010);Hovy (1987); Bateman and Paris (1989) (see (Mairesse and Walker, 2011) for a comprehensive review). Among these, the work of Power et al (2003), like ours, allows the user to control various stylistic aspects of the generated text.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Workshop on Stylistic Variation

Shoemark¹,

Goldwater²,

Kirby

2017

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The Workshop on Stylistic Variation (StyVa) at EMNLP 2017 is the first of its kind, offering a new venue for bringing together a large but previously underserved and splintered community within computational linguistics. Our goal in creating this workshop was to attract a variety of perspectives on style from traditional areas within NLP, including authorship attribution, author profiling, genre studies, natural language generation, distributional lexicography, and literary and educational applications; to this end we have defined stylistic variation as broadly as possible, to include any variation in phonological, lexical, syntactic, or discourse realization of particular semantic content, due to differences in extralinguistic variables such as individual speaker, speaker demographics, target audience, genre, etc.We received 22 submissions, of which we accepted 14 (64%), seven as talks and seven as poster presentations.Though there was indeed a great deal of diversity in the submissions, including at least one submission in several of the major topic areas discussed above, we also noted a clear trend: we received several papers on style-sensitive language generation, particularly using neural network models. This clearly reflects a more general interest in the field, and one we would expect to continue. More generally, we are pleased that this workshop has served as a venue for both traditional and cutting-edge approaches to style.We'd like to thank the authors for choosing StyVa as a venue for their excellent work, our invited speakers (Ani Nenkova and Walter Daelemans) for their invaluable contribution, and of course the reviews provided by our esteemed Program Committee. We'd also want to thank the ACL workshop organizing committee for giving us this opportunity to bring together the NLP stylistic community.We look forward to a great workshop in Copenhagen! Julian Brooke, Moshe Koppel, and Thamar Solorio AbstractAs natural language processing research is growing and largely driven by the availability of data, we expanded research from news and small-scale dialog corpora to web and social media. User-generated data and crowdsourcing opened the door for investigating human language of various styles with more statistical power and real-world applications. In this position/survey paper, I will review and discuss seven language styles that I believe to be important and interesting to study: influential work in the past, challenges at the present, and potential impact for the future. Top Three ProblemsThe top three problems for studying language styles are data, data and data. More specifically, they are data shortage, data fusion, and data annotation problems. The data shortage problem has been improving, which is the main reason that there is surge in the number of research studies on language styles. The data fusion problem is more specific to the area, due to the subtle and often subjective nature of linguistic styles. For instance, while men and women talk in different ways (note this is not the same as ta...

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“…Over the last 50 years, the Big Five model of human personality has become widely accepted in psychology [Funder 1997;Goldberg 1990;Norman 1963], and is starting to serve as a framework for modeling [Mairesse and Walker 2011;2007;2008], and gestural variations [Neff et al 2010;Neff et al 2011]. It consists of the personality traits: extraversion, emotional stability (EMS, also called by its opposite pole, neuroticism), agreeableness, conscientiousness and openness to experience, where adjectives associated with these traits are listed in Table I.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Impact of Hand Motion on Virtual Character Personality

Wang

Tree

Walker

et al. 2016

ACM Trans. Appl. Percept.

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Designing virtual characters that are capable of conveying a sense of personality is important for generating realistic experiences, and thus a key goal in computer animation research. Though the influence of gesture and body motion on personality perception has been studied, little is known about which attributes of hand pose and motion convey particular personality traits. Using the "Big Five" model as a framework for evaluating personality traits, this work examines how variations in hand pose and motion impact the perception of a character's personality. As has been done with facial motion, we first study hand motion in isolation as a requirement for running controlled experiments that avoid the combinatorial explosion of multimodal communication (all combinations of facial expressions, arm movements, body movements and hands) and allow us to understand the communicative content of hands. We determined a set of features likely to reflect personality, based on research in psychology and previous human motion perception work: shape, direction, amplitude, speed and manipulation. Then we captured realistic hand motion varying these attributes, and conducted three perceptual experiments to determine the contribution of these attributes to the character's personalities. Both hand poses and the amplitude of hand motion affected the perception of all five personality traits. Speed impacted all traits except openness. Direction impacted extraversion and openness. Manipulation was perceived as an indicator of introversion, disagreeableness, neuroticism and less openness to experience. From these results, we generalize guidelines for designing detailed hand motion that can add to the expressiveness and personality of characters. We performed an evaluation study that combined hand motion with gesture and body motion. Even in the presence of body motion, hand motion still significantly impacted the perception of a character's personality and could even be the dominant factor in certain situations.

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Controlling User Perceptions of Linguistic Style: Trainable Generation of Personality Traits

Cited by 151 publications

References 45 publications

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Don’t Scratch! Self-adaptors Reflect Emotional Stability

Proceedings of the Workshop on Stylistic Variation

Assessing the Impact of Hand Motion on Virtual Character Personality

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