{"title":"SCBG:情感支持对话的语义约束双向生成","authors":"Yangyang Xu, Zhuoer Zhao, Xiao Sun","doi":"10.1145/3666090","DOIUrl":null,"url":null,"abstract":"<p>The Emotional Support Conversation (ESC) task aims to deliver consolation, encouragement, and advice to individuals undergoing emotional distress, thereby assisting them in overcoming difficulties. In the context of emotional support dialogue systems, it is of utmost importance to generate user-relevant and diverse responses. However, previous methods failed to take into account these crucial aspects, resulting in a tendency to produce universal and safe responses (e.g., “I do not know” and “I am sorry to hear that”). To tackle this challenge, a semantic-constrained bidirectional generation (SCBG) framework is utilized for generating more diverse and user-relevant responses. Specifically, we commence by selecting keywords that encapsulate the ongoing dialogue topics based on the context. Subsequently, a bidirectional generator generates responses incorporating these keywords. Two distinct methodologies, namely statistics-based and prompt-based methods, are employed for keyword extraction. Experimental results on the ESConv dataset demonstrate that the proposed SCBG framework improves response diversity and user relevance while ensuring response quality.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SCBG: Semantic-Constrained Bidirectional Generation for Emotional Support Conversation\",\"authors\":\"Yangyang Xu, Zhuoer Zhao, Xiao Sun\",\"doi\":\"10.1145/3666090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The Emotional Support Conversation (ESC) task aims to deliver consolation, encouragement, and advice to individuals undergoing emotional distress, thereby assisting them in overcoming difficulties. In the context of emotional support dialogue systems, it is of utmost importance to generate user-relevant and diverse responses. However, previous methods failed to take into account these crucial aspects, resulting in a tendency to produce universal and safe responses (e.g., “I do not know” and “I am sorry to hear that”). To tackle this challenge, a semantic-constrained bidirectional generation (SCBG) framework is utilized for generating more diverse and user-relevant responses. Specifically, we commence by selecting keywords that encapsulate the ongoing dialogue topics based on the context. Subsequently, a bidirectional generator generates responses incorporating these keywords. Two distinct methodologies, namely statistics-based and prompt-based methods, are employed for keyword extraction. Experimental results on the ESConv dataset demonstrate that the proposed SCBG framework improves response diversity and user relevance while ensuring response quality.</p>\",\"PeriodicalId\":54312,\"journal\":{\"name\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-05-27\",\"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/10.1145/3666090\",\"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/10.1145/3666090","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}
SCBG: Semantic-Constrained Bidirectional Generation for Emotional Support Conversation
The Emotional Support Conversation (ESC) task aims to deliver consolation, encouragement, and advice to individuals undergoing emotional distress, thereby assisting them in overcoming difficulties. In the context of emotional support dialogue systems, it is of utmost importance to generate user-relevant and diverse responses. However, previous methods failed to take into account these crucial aspects, resulting in a tendency to produce universal and safe responses (e.g., “I do not know” and “I am sorry to hear that”). To tackle this challenge, a semantic-constrained bidirectional generation (SCBG) framework is utilized for generating more diverse and user-relevant responses. Specifically, we commence by selecting keywords that encapsulate the ongoing dialogue topics based on the context. Subsequently, a bidirectional generator generates responses incorporating these keywords. Two distinct methodologies, namely statistics-based and prompt-based methods, are employed for keyword extraction. Experimental results on the ESConv dataset demonstrate that the proposed SCBG framework improves response diversity and user relevance while ensuring response quality.
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.