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

6 Dec 2016ACL 2017 7arXiv:1612.01627archive 2025-07-28

Yu Wu, Wei Wu, Chen Xing, Ming Zhou, Zhoujun Li

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.

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MarkWuNLP/MultiTurnResponseSelection officialmentioned in papertf report
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Tasks

Conversational Response SelectionRetrieval

Datasets

Introduced by this paper, per the archive.

DoubanDouban Conversation Corpus

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Conversational Response Selection Douban SMN MAP 0.529 #16 of 16 Archive leaderboard report
Conversational Response Selection Douban SMN MRR 0.569 #16 of 16 Archive leaderboard report
Conversational Response Selection Douban SMN P@1 0.397 #16 of 16 Archive leaderboard report
Conversational Response Selection Douban SMN R10@1 0.233 #16 of 16 Archive leaderboard report
Conversational Response Selection Douban SMN R10@2 0.396 #16 of 16 Archive leaderboard report
Conversational Response Selection Douban SMN R10@5 0.724 #16 of 16 Archive leaderboard report
Conversational Response Selection E-commerce SMN R10@1 0.453 #15 of 15 Archive leaderboard report
Conversational Response Selection E-commerce SMN R10@2 0.654 #15 of 15 Archive leaderboard report
Conversational Response Selection E-commerce SMN R10@5 0.886 #15 of 15 Archive leaderboard report
Conversational Response Selection RRS SMN MAP 0.487 #7 of 7 Archive leaderboard report
Conversational Response Selection RRS SMN MRR 0.501 #7 of 7 Archive leaderboard report
Conversational Response Selection RRS SMN P@1 0.309 #7 of 7 Archive leaderboard report
Conversational Response Selection RRS SMN R10@1 0.281 #7 of 7 Archive leaderboard report
Conversational Response Selection RRS SMN R10@2 0.442 #7 of 7 Archive leaderboard report
Conversational Response Selection RRS SMN R10@5 0.723 #7 of 7 Archive leaderboard report
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) SMN R10@1 0.726 #22 of 25 Archive leaderboard report
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) SMN R10@2 0.822 #22 of 25 Archive leaderboard report
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) SMN R10@5 0.960 #22 of 25 Archive leaderboard report
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) SMN R2@1 0.926 #22 of 25 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution

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