Supervised attention for answer selection in community question answering

Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores matchLSTM for answer selection in CQA. A lexi...

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Bibliographic Details
Published inIAES International Journal of Artificial Intelligence Vol. 9; no. 2; p. 203
Main Authors Ha, Thanh Thi, Takasu, Atsuhiro, Nguyen, Thanh Chinh, Nguyen, Kiem Hieu, Nguyen, Van Nha, Nguyen, Kim Anh, Tran, Son Giang
Format Journal Article
LanguageEnglish
Published Yogyakarta IAES Institute of Advanced Engineering and Science 01.06.2020
Subjects
Natural language processing
Neural networks
Questions
Sentences
Words (language)
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Summary:Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores matchLSTM for answer selection in CQA. A lexical gap in CQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into matchLSTM . Specifically, we leverage lexical-semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.
ISSN:2089-4872
2252-8938
2089-4872
DOI:10.11591/ijai.v9.i2.pp203-211