CharacterChat: Learning towards Conversational AI with Personalized
Social Support
- URL: http://arxiv.org/abs/2308.10278v1
- Date: Sun, 20 Aug 2023 14:24:26 GMT
- Title: CharacterChat: Learning towards Conversational AI with Personalized
Social Support
- Authors: Quan Tu, Chuanqi Chen, Jinpeng Li, Yanran Li, Shuo Shang, Dongyan
Zhao, Ran Wang, Rui Yan
- Abstract summary: We introduce the Social Support Conversation (S2Conv) framework.
It comprises a series of support agents and the interpersonal matching mechanism, linking individuals with persona-compatible virtual supporters.
We present CharacterChat, a comprehensive S2Conv system, which includes a conversational model driven by personas and memories.
- Score: 61.20396854093821
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In our modern, fast-paced, and interconnected world, the importance of mental
well-being has grown into a matter of great urgency. However, traditional
methods such as Emotional Support Conversations (ESC) face challenges in
effectively addressing a diverse range of individual personalities. In
response, we introduce the Social Support Conversation (S2Conv) framework. It
comprises a series of support agents and the interpersonal matching mechanism,
linking individuals with persona-compatible virtual supporters. Utilizing
persona decomposition based on the MBTI (Myers-Briggs Type Indicator), we have
created the MBTI-1024 Bank, a group that of virtual characters with distinct
profiles. Through improved role-playing prompts with behavior preset and
dynamic memory, we facilitate the development of the MBTI-S2Conv dataset, which
contains conversations between the characters in the MBTI-1024 Bank. Building
upon these foundations, we present CharacterChat, a comprehensive S2Conv
system, which includes a conversational model driven by personas and memories,
along with an interpersonal matching plugin model that dispatches the optimal
supporters from the MBTI-1024 Bank for individuals with specific personas.
Empirical results indicate the remarkable efficacy of CharacterChat in
providing personalized social support and highlight the substantial advantages
derived from interpersonal matching. The source code is available in
\url{https://github.com/morecry/CharacterChat}.
Related papers
- A Chinese Multi-label Affective Computing Dataset Based on Social Media Network Users [2.0209172586699173]
This study collected data from the major social media platform Weibo, screening 11,338 valid users from over 50,000 individuals with diverse MBTI personality labels.
We compiled a multi-label Chinese affective computing dataset that integrates the same user's personality traits with six emotions and micro-emotions, each annotated with intensity levels.
This dataset is designed to advance machine recognition of complex human emotions and provide data support for research in psychology, education, marketing, finance, and politics.
arXiv Detail & Related papers (2024-11-13T05:38:55Z) - Social Support Detection from Social Media Texts [44.096359084699]
Social support, conveyed through a multitude of interactions and platforms such as social media, plays a pivotal role in fostering a sense of belonging.
This paper introduces Social Support Detection (SSD) as a Natural language processing (NLP) task aimed at identifying supportive interactions.
We conducted experiments on a dataset comprising 10,000 YouTube comments.
arXiv Detail & Related papers (2024-11-04T20:23:03Z) - Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues [63.936654900356004]
Personality recognition aims to identify the personality traits implied in user data such as dialogues and social media posts.
We propose a novel task named Explainable Personality Recognition, aiming to reveal the reasoning process as supporting evidence of the personality trait.
arXiv Detail & Related papers (2024-09-29T14:41:43Z) - Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation [30.820334868031537]
Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content.
We propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC.
arXiv Detail & Related papers (2024-04-03T09:14:24Z) - CharacterGLM: Customizing Chinese Conversational AI Characters with
Large Language Models [66.4382820107453]
We present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters.
Our CharacterGLM is designed for generating Character-based Dialogues (CharacterDial), which aims to equip a conversational AI system with character customization for satisfying people's inherent social desires and emotional needs.
arXiv Detail & Related papers (2023-11-28T14:49:23Z) - MPCHAT: Towards Multimodal Persona-Grounded Conversation [54.800425322314105]
We extend persona-based dialogue to the multimodal domain and make two main contributions.
First, we present the first multimodal persona-based dialogue dataset named MPCHAT.
Second, we empirically show that incorporating multimodal persona, as measured by three proposed multimodal persona-grounded dialogue tasks, leads to statistically significant performance improvements.
arXiv Detail & Related papers (2023-05-27T06:46:42Z) - Exploring Personality and Online Social Engagement: An Investigation of
MBTI Users on Twitter [0.0]
We investigate 3848 profiles from Twitter with self-labeled Myers-Briggs personality traits (MBTI)
We leverage BERT, a state-of-the-art NLP architecture based on deep learning, to analyze various sources of text that hold most predictive power for our task.
We find that biographies, statuses, and liked tweets contain significant predictive power for all dimensions of the MBTI system.
arXiv Detail & Related papers (2021-09-14T02:26:30Z) - Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia
for Human Personality Profiling [74.83957286553924]
We infer the Myers-Briggs Personality Type indicators by applying a novel multi-view fusion framework, called "PERS"
Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources.
arXiv Detail & Related papers (2021-06-20T10:48:49Z) - My tweets bring all the traits to the yard: Predicting personality and
relational traits in Online Social Networks [4.095574580512599]
This study aims to provide a prediction model for a holistic personality profiling in Online Social Networks (OSNs)
We first designed a feature engineering methodology that extracts a wide range of features from OSN accounts of users.
Then, we designed a machine learning model that predicts scores for the psychological traits of the users based on the extracted features.
arXiv Detail & Related papers (2020-09-22T20:30:56Z) - Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset
for Personality Assessment [50.15466026089435]
We present a novel peer-to-peer Hindi conversation dataset- Vyaktitv.
It consists of high-quality audio and video recordings of the participants, with Hinglish textual transcriptions for each conversation.
The dataset also contains a rich set of socio-demographic features, like income, cultural orientation, amongst several others, for all the participants.
arXiv Detail & Related papers (2020-08-31T17:44:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.