Zhipeng Liu;Jing He;Tao Gong;Heng Weng;Fu Lee Wang;Hai Liu;Tianyong Hao
{"title":"Improving Topic Tracing with a Textual Reader for Conversational Knowledge Based Question Answering","authors":"Zhipeng Liu;Jing He;Tao Gong;Heng Weng;Fu Lee Wang;Hai Liu;Tianyong Hao","doi":"10.1109/TETCI.2024.3369478","DOIUrl":null,"url":null,"abstract":"Conversational KBQA(Knowledge Based Question Answering) is a sequential question-answering process in the form of conversation based on knowledge, and it has been paid great attention in recent years. One of the major challenges in conversational KBQA is the ellipsis and co-reference of topic entities in follow-up questions, which affects the performance of the whole conversational KBQA. Previous approaches identified the topics of current turn questions by encoding conversation records or modeling entities in conversation records. However, they ignored the meanings carried by the entities themselves in the modeling process. To solve the above problem and mitigate the impact of the problem on the whole KBQA system, we propose a new textual reader to integrate entity-related textual information and construct a graph-based neural network containing the textual reader to determine the topics of questions. The graph-based neural network scores entities in each question in conversations. Further, the scores are jointly cooperated with the similarity between questions and answers to obtain the correct answers in conversational KBQA systems. Our proposed method improved the accuracy with 5.5% at topic entity prediction and 1.5% at conversational KBQA on benchmark datasets compared with baseline methods in more real-world settings respectively. Experiment results on two datasets demonstrate that our proposed method improves the performance of topic tracing and conversational KBQA.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10466789/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Conversational KBQA(Knowledge Based Question Answering) is a sequential question-answering process in the form of conversation based on knowledge, and it has been paid great attention in recent years. One of the major challenges in conversational KBQA is the ellipsis and co-reference of topic entities in follow-up questions, which affects the performance of the whole conversational KBQA. Previous approaches identified the topics of current turn questions by encoding conversation records or modeling entities in conversation records. However, they ignored the meanings carried by the entities themselves in the modeling process. To solve the above problem and mitigate the impact of the problem on the whole KBQA system, we propose a new textual reader to integrate entity-related textual information and construct a graph-based neural network containing the textual reader to determine the topics of questions. The graph-based neural network scores entities in each question in conversations. Further, the scores are jointly cooperated with the similarity between questions and answers to obtain the correct answers in conversational KBQA systems. Our proposed method improved the accuracy with 5.5% at topic entity prediction and 1.5% at conversational KBQA on benchmark datasets compared with baseline methods in more real-world settings respectively. Experiment results on two datasets demonstrate that our proposed method improves the performance of topic tracing and conversational KBQA.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.