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arXiv:1612.01627
37. Wu, Y., Wu, W., Xing, C., Zhou, M., Li, Z.: Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots. <anchor>arXiv:1612.01627</anchor> [cs] (2016)
[1612.01627] Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
\[1612.01627\] Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots

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# Computer Science > Computation and Language

**arXiv:1612.01627** (cs)


\[Submitted on 6 Dec 2016 ( [v1](https://arxiv.org/abs/1612.01627v1)), last revised 15 May 2017 (this version, v2)\]

# Title:Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots

Authors: [Yu Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+Y), [Wei Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+W), [Chen Xing](https://arxiv.org/search/cs?searchtype=author&query=Xing,+C), [Ming Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+M), [Zhoujun Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+Z)

View a PDF of the paper titled Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots, by Yu Wu and 4 other authors

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> Abstract:We study response selection for multi-turn conversation in retrieval-based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among utterances. The final matching score is calculated with the hidden states of the RNN. An empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation.

Comments:ACL 2017Subjects:Computation and Language (cs.CL)Cite as:[arXiv:1612.01627](https://arxiv.org/abs/1612.01627) \[cs.CL\](or [arXiv:1612.01627v2](https://arxiv.org/abs/1612.01627v2) \[cs.CL\] for this version)
[https://doi.org/10.48550/arXiv.1612.01627](https://doi.org/10.48550/arXiv.1612.01627)

Focus to learn more

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## Submission history

From: Yu Wu \[ [view email](/show-email/e93666f9/1612.01627)\]

**[\[v1\]](/abs/1612.01627v1)**
Tue, 6 Dec 2016 01:57:39 UTC (608 KB)

**\[v2\]**
Mon, 15 May 2017 01:50:55 UTC (600 KB)

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