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arXiv · 2005.09276

Matching Questions and Answers in Dialogues from Online Forums

Qi Jia·Mengxue Zhang·Shengyao Zhang·Kenny Q. Zhu
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Abstract

Matching question-answer relations between two turns in conversations is not only the first step in analyzing dialogue structures, but also valuable for training dialogue systems. This paper presents a QA matching model considering both distance information and dialogue history by two simultaneous attention mechanisms called mutual attention. Given scores computed by the trained model between each non-question turn with its candidate questions, a greedy matching strategy is used for final predictions. Because existing dialogue datasets such as the Ubuntu dataset are not suitable for the QA matching task, we further create a dataset with 1,000 labeled dialogues and demonstrate that our proposed model outperforms the state-of-the-art and other strong baselines, particularly for matching long-distance QA pairs.

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Qi Jia, Mengxue Zhang, Shengyao Zhang, Kenny Q. Zhu. 2020-05-19. Matching Questions and Answers in Dialogues from Online Forums. https://doi.org/10.3233/faia200326

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