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        <title>Modeling Multi-turn Conversation with Deep Utterance Aggregation</title>
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        <namePart type="given">Zhuosheng</namePart>
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        <namePart type="given">Jiangtong</namePart>
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    <abstract>Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus.</abstract>
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%0 Conference Proceedings
%T Modeling Multi-turn Conversation with Deep Utterance Aggregation
%A Zhang, Zhuosheng
%A Li, Jiangtong
%A Zhu, Pengfei
%A Zhao, Hai
%A Liu, Gongshen
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F zhang-etal-2018-modeling
%X Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus.
%U https://aclanthology.org/C18-1317
%P 3740-3752

Markdown (Informal)

[Modeling Multi-turn Conversation with Deep Utterance Aggregation](https://aclanthology.org/C18-1317) (Zhang et al., COLING 2018)

• Modeling Multi-turn Conversation with Deep Utterance Aggregation (Zhang et al., COLING 2018)

ACL

• Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, Hai Zhao, and Gongshen Liu. 2018. Modeling Multi-turn Conversation with Deep Utterance Aggregation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3740–3752, Santa Fe, New Mexico, USA. Association for Computational Linguistics.