XMQAs: Constructing Complex-Modified Question-Answering Dataset for Robust Question Understanding

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(2024)

引用 0|浏览38
暂无评分
摘要
Question understanding is an important issue to the success of a Knowledge-based Question Answering (KBQA) system.However, the existing study does not pay enough attention to this issue given that the questions in the existing KBQA datasets are usually expressed in simple and straightforward way. This is not in line with the actual linguistic conventions, which often use a lot of modifiers. To facilitate the study on evaluating and enhancing the question understanding ability of the KBQA systems, this paper proposes to construct a complex-modified question-answering (XMQAs) dataset based on existing KBQA datasets. With the help of knowledge bases and dictionaries, three kinds of modifiers are defined and applied to original simple-expressed questions. These modifiers could make the expression of these questions complex without changing their semantics. Based on XMQAs, we then propose a novel question understanding algorithm upon existing KBQA models, which greatly improves the robustness of their question understanding abilities. We conduct extensive experiments on XMQAs and two widely acknowledged KBQA datasets. The empirical results demonstrate that our proposed algorithm can improve the performance of KBQA models on not only the complex-modified questions, but also simple-expressed questions.
更多
查看译文
关键词
Knowledge based systems,Tail,Dictionaries,Semantics,Question answering (information retrieval),Online services,Internet,Complex-modified questions,question generation,question understanding,knowledge-based question answering
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要