心理聊天机器人基于认知行为疗法、面向中国抑郁人群的心理咨询机器人

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-07-05 DOI:10.1145/3676962
Tiantian Chen, Ying Shen, Xuri Chen, Lin Zhang
{"title":"心理聊天机器人基于认知行为疗法、面向中国抑郁人群的心理咨询机器人","authors":"Tiantian Chen, Ying Shen, Xuri Chen, Lin Zhang","doi":"10.1145/3676962","DOIUrl":null,"url":null,"abstract":"Nowadays, depression has been widely concerned due to the growing depressed population. Depression is a global mental problem, the worst case of which can lead to suicide. However, factors such as high treatment costs and social stigma prevent people from obtaining effective treatments. Chatbot technology is one of the main attempts to solve the problem. But as far as we know, existing chatbot systems designed for depressed people are still sporadic, and most of them have some non-negligible limitations. Specifically, existing systems simply guide users to release their negative emotions or provide some general advice. They cannot offer personalized advice for users’ specific problems. In addition, most of them only support English speakers, despite the fact that depressed Chinese constitute a large population. Psychological counseling systems for the depressed Chinese population with improved responsiveness are temporarily lacking. As an attempt to fill in the research gap to some extent, we design a novel Chinese psychological chatbot system, namely PsyChatbot. First, we establish a counseling dialogue framework based on Cognitive Behavioral Therapy (CBT), which guides users to reflect on themselves and helps them discover their negative perceptions. Then, we propose a retrieval-based Q&A algorithm to provide suitable suggestions for users’ specific problems. Last but not least, we construct a large-scale Chinese counseling Q&A corpus, which contains nearly 89,000 psychological Q&A triples. Experimental results have demonstrated the effectiveness of PsyChatbot. The source code and data has been released at https://github.com/slptongji/PsyChatbot.","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PsyChatbot: A Psychological Counseling Agent Towards Depressed Chinese Population Based on Cognitive Behavioural Therapy\",\"authors\":\"Tiantian Chen, Ying Shen, Xuri Chen, Lin Zhang\",\"doi\":\"10.1145/3676962\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, depression has been widely concerned due to the growing depressed population. Depression is a global mental problem, the worst case of which can lead to suicide. However, factors such as high treatment costs and social stigma prevent people from obtaining effective treatments. Chatbot technology is one of the main attempts to solve the problem. But as far as we know, existing chatbot systems designed for depressed people are still sporadic, and most of them have some non-negligible limitations. Specifically, existing systems simply guide users to release their negative emotions or provide some general advice. They cannot offer personalized advice for users’ specific problems. In addition, most of them only support English speakers, despite the fact that depressed Chinese constitute a large population. Psychological counseling systems for the depressed Chinese population with improved responsiveness are temporarily lacking. As an attempt to fill in the research gap to some extent, we design a novel Chinese psychological chatbot system, namely PsyChatbot. First, we establish a counseling dialogue framework based on Cognitive Behavioral Therapy (CBT), which guides users to reflect on themselves and helps them discover their negative perceptions. Then, we propose a retrieval-based Q&A algorithm to provide suitable suggestions for users’ specific problems. Last but not least, we construct a large-scale Chinese counseling Q&A corpus, which contains nearly 89,000 psychological Q&A triples. Experimental results have demonstrated the effectiveness of PsyChatbot. The source code and data has been released at https://github.com/slptongji/PsyChatbot.\",\"PeriodicalId\":54312,\"journal\":{\"name\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3676962\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Asian and Low-Resource Language Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3676962","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

如今,由于抑郁症人口不断增加,抑郁症已受到广泛关注。抑郁症是一种全球性的精神问题,最严重的可导致自杀。然而,高昂的治疗费用和社会耻辱感等因素阻碍了人们获得有效的治疗。聊天机器人技术是解决这一问题的主要尝试之一。但据我们所知,现有的针对抑郁症患者的聊天机器人系统还很零散,而且大多数都存在一些不可忽视的局限性。具体来说,现有系统只是简单地引导用户释放负面情绪或提供一些一般性建议。它们无法针对用户的具体问题提供个性化建议。此外,尽管抑郁的中国人数量庞大,但大多数系统只支持英语使用者。目前,针对中国抑郁人群的心理咨询系统还缺乏响应速度更快的系统。为了在一定程度上填补这一研究空白,我们设计了一个新颖的中文心理聊天机器人系统,即心理聊天机器人(PsyChatbot)。首先,我们建立了一个基于认知行为疗法(CBT)的心理咨询对话框架,引导用户反思自己,帮助他们发现自己的负面认知。然后,我们提出了一种基于检索的问答算法,针对用户的具体问题提供合适的建议。最后,我们构建了一个大规模的中文心理咨询问答语料库,其中包含近 89,000 个心理问答三元组。实验结果证明了 PsyChatbot 的有效性。源代码和数据已在 https://github.com/slptongji/PsyChatbot 上发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
PsyChatbot: A Psychological Counseling Agent Towards Depressed Chinese Population Based on Cognitive Behavioural Therapy
Nowadays, depression has been widely concerned due to the growing depressed population. Depression is a global mental problem, the worst case of which can lead to suicide. However, factors such as high treatment costs and social stigma prevent people from obtaining effective treatments. Chatbot technology is one of the main attempts to solve the problem. But as far as we know, existing chatbot systems designed for depressed people are still sporadic, and most of them have some non-negligible limitations. Specifically, existing systems simply guide users to release their negative emotions or provide some general advice. They cannot offer personalized advice for users’ specific problems. In addition, most of them only support English speakers, despite the fact that depressed Chinese constitute a large population. Psychological counseling systems for the depressed Chinese population with improved responsiveness are temporarily lacking. As an attempt to fill in the research gap to some extent, we design a novel Chinese psychological chatbot system, namely PsyChatbot. First, we establish a counseling dialogue framework based on Cognitive Behavioral Therapy (CBT), which guides users to reflect on themselves and helps them discover their negative perceptions. Then, we propose a retrieval-based Q&A algorithm to provide suitable suggestions for users’ specific problems. Last but not least, we construct a large-scale Chinese counseling Q&A corpus, which contains nearly 89,000 psychological Q&A triples. Experimental results have demonstrated the effectiveness of PsyChatbot. The source code and data has been released at https://github.com/slptongji/PsyChatbot.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.60
自引率
15.00%
发文量
241
期刊介绍: The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.
期刊最新文献
Learning and Vision-based approach for Human fall detection and classification in naturally occurring scenes using video data A DENSE SPATIAL NETWORK MODEL FOR EMOTION RECOGNITION USING LEARNING APPROACHES CNN-Based Models for Emotion and Sentiment Analysis Using Speech Data TRGCN: A Prediction Model for Information Diffusion Based on Transformer and Relational Graph Convolutional Network Adaptive Semantic Information Extraction of Tibetan Opera Mask with Recall Loss
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1