SCBG:情感支持对话的语义约束双向生成

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 : 2024-05-27 DOI:10.1145/3666090
Yangyang Xu, Zhuoer Zhao, Xiao Sun
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引用次数: 0

摘要

情感支持对话(ESC)任务旨在向受到情感困扰的个人提供安慰、鼓励和建议,从而帮助他们克服困难。在情感支持对话系统中,最重要的是生成与用户相关的多样化回复。然而,以往的方法没有考虑到这些关键方面,导致产生的回复往往是通用和安全的(如 "我不知道 "和 "很遗憾听到这个消息")。为了应对这一挑战,我们采用了语义约束双向生成(SCBG)框架,以生成更加多样化和与用户相关的回复。具体来说,我们首先根据上下文选择能概括当前对话主题的关键词。随后,双向生成器生成包含这些关键词的回复。关键字提取采用了两种不同的方法,即基于统计的方法和基于提示的方法。在 ESConv 数据集上的实验结果表明,所提出的 SCBG 框架在确保回复质量的同时,还提高了回复的多样性和用户相关性。
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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.

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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.
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