Towards Automated Dialog Personalization using MBTI Personality Indicators

Fernau, Daniel; Hillmann, Stefan; Feldhus, Nils; Polzehl, Tim

doi:10.21437/interspeech.2022-376

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Big Five model, for example, encapsulates five core personality traits: extroversion, agreeableness, conscientiousness, neuroticism, and openness [55]. However, Völkel et al [86] questioned the comprehensiveness of the Big Five model, prompting further investigations [29,64] into alternative frameworks, such as the Myers-Briggs Type Indicator (MBTI). Yet, these studies largely focus on fixed or domain-specific persona traits determined by researchers, leading to a lack of deeper and broader understanding of how real users adjust and personalize the persona of conversational agents in various situations.…”

Section: Elements Of Customizing Agent Personasmentioning

confidence: 99%

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

Ha,

Jeon,

Han

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent personas with distinct expertise or traits can address this issue. Nonetheless, we lack knowledge of how people customize and interact with agent personas. In this

show abstract

Section: Elements Of Customizing Agent Personasmentioning

confidence: 99%

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

Ha,

Jeon,

Han

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…Emotion depends on individual users, e.g., their personality traits (such as the Big Five (Goldberg, 1990)). Personality traits also play an important role in a variety of user-adapted interactions (Mairesse and Walker, 2010;Mota et al, 2018;Fernau et al, 2022;Yamamoto et al, 2023). The personality traits of a robot and human interlocutors are known to be effective for engagement estimation in human-robot interactions (Salam et al, 2017), and correlation between the engagement and the personality traits given per dialogue has been investigated in human-robot and human-human interactions (Celiktutan et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Analyzing Differences in Subjective Annotations by Participants and Third-party Annotators in Multimodal Dialogue Corpus

Komatani,

Takeda,

Okada

2023

Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue

View full text Add to dashboard Cite

Estimating the subjective impressions of human users during a dialogue is necessary when constructing a dialogue system that can respond adaptively to their emotional states. However, such subjective impressions (e.g., how much the user enjoys the dialogue) are inherently ambiguous, and the annotation results provided by multiple annotators do not always agree because they depend on the subjectivity of the annotators. In this paper, we analyzed the annotation results using 13,226 exchanges from 155 participants in a multimodal dialogue corpus called Hazumi that we had constructed, where each exchange was annotated by five third-party annotators. We investigated the agreement between the subjective annotations given by the third-party annotators and the participants themselves, on both perexchange annotations (i.e., participant's sentiments) and per-dialogue (-participant) annotations (i.e., questionnaires on rapport and personality traits). We also investigated the conditions under which the annotation results are reliable. Our findings demonstrate that the dispersion of third-party sentiment annotations correlates with agreeableness of the participants, one of the Big Five personality traits.

show abstract

“…For example, an extroverted and cheerful person may be more likely to express positive emotions, whereas an introverted and nervous person may be more inclined to experience negative emotions. Thus, incorporating personality factors into models of emotion generation can more accurately model and predict an individual's emotional responses and provide more personalized and adaptive emotion generation [5].…”

Section: Introductionmentioning

confidence: 99%

Personality Enhanced Emotion Generation Modeling for Dialogue Systems

Ma,

Jia,

Zhou

et al. 2023

Cogn Comput

View full text Add to dashboard Cite

Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from BigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user’s unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

show abstract

Towards Automated Dialog Personalization using MBTI Personality Indicators

Cited by 7 publications

References 0 publications

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

Analyzing Differences in Subjective Annotations by Participants and Third-party Annotators in Multimodal Dialogue Corpus

Personality Enhanced Emotion Generation Modeling for Dialogue Systems

Contact Info

Product

Resources

About