标题:From Question to Text: Question-Oriented Feature Attention for Answer Selection
作者:Huang, Heyan; Wei, Xiaochi; Nie, Liqiang; Mao, Xianling; Xu, Xin-Shun
作者机构:[Huang, Heyan; Wei, Xiaochi; Mao, Xianling] Beijing Inst Technol, Sch Comp, Beijing Engn Res Ctr High Volume Language Informa, 5 Zhongguancun South St 更多
通讯作者:Wei, XC
通讯作者地址:[Wei, XC]Beijing Inst Technol, Sch Comp, Beijing Engn Res Ctr High Volume Language Informa, 5 Zhongguancun South St, Beijing 100081, Peoples R China.
来源:ACM TRANSACTIONS ON INFORMATION SYSTEMS
出版年:2019
卷:37
期:1
DOI:10.1145/3233771
关键词:Question answering; answer selection; attention method
摘要:Understanding unstructured texts is an essential skill for human beings as it enables knowledge acquisition. Although understanding unstructured texts is easy for we human beings with good education, it is a great challenge for machines. Recently, with the rapid development of artificial intelligence techniques, researchers put efforts to teach machines to understand texts and justify the educated machines by letting them solve the questions upon the given unstructured texts, inspired by the reading comprehension test as we humans do. However, feature effectiveness with respect to different questions significantly hinders the performance of answer selection, because different questions may focus on various aspects of the given text and answer candidates. To solve this problem, we propose a question-oriented feature attention (QFA) mechanism, which learns to weight different engineering features according to the given question, so that important features with respect to the specific question is emphasized accordingly. Experiments on MCTest dataset have well validated the effectiveness of the proposed method. Additionally, the proposed QFA is applicable to various IR tasks, such as question answering and answer selection. We have verified the applicability on a crawled community-based question-answering dataset.
收录类别:SCIE
资源类型:期刊论文
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