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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Hybrid Attentive Answer Selection in CQA With Deep Users Modelling

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Jiahui Wen

The University of Queensland


Jingwei Ma

The University of Queensland


Yiliu Feng

National University of Defence Technology


Mingyang Zhong

The University of Queensland


DOI:

10.1609/aaai.v32i1.11840


Abstract:

In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.

Topics: AAAI

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HOW TO CITE:

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong Hybrid Attentive Answer Selection in CQA With Deep Users Modelling Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong Hybrid Attentive Answer Selection in CQA With Deep Users Modelling AAAI 2018, .

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong (2018). Hybrid Attentive Answer Selection in CQA With Deep Users Modelling. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong. Hybrid Attentive Answer Selection in CQA With Deep Users Modelling. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong. 2018. Hybrid Attentive Answer Selection in CQA With Deep Users Modelling. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong. (2018) "Hybrid Attentive Answer Selection in CQA With Deep Users Modelling", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong, "Hybrid Attentive Answer Selection in CQA With Deep Users Modelling", AAAI, p., 2018.

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong. "Hybrid Attentive Answer Selection in CQA With Deep Users Modelling". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong. "Hybrid Attentive Answer Selection in CQA With Deep Users Modelling". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Jiahui Wen||Jingwei Ma||Yiliu Feng||Mingyang Zhong. Hybrid Attentive Answer Selection in CQA With Deep Users Modelling. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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