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This motivates us to propose a new matching framework that can sufficiently carry important information in contexts to matching and model relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interact with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) that models relationships among utterances. Context-response matching is then calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experiment results show that both models can significantly outperform state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage important information in contexts for matching.<\/jats:p>","DOI":"10.1162\/coli_a_00345","type":"journal-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T11:12:19Z","timestamp":1545390739000},"page":"163-197","source":"Crossref","is-referenced-by-count":44,"title":["A Sequential Matching Framework for Multi-Turn Response Selection in Retrieval-Based Chatbots"],"prefix":"10.1162","volume":"45","author":[{"given":"Yu","family":"Wu","sequence":"first","affiliation":[{"name":"Beihang University, State Key Laboratory of Software Development Environment."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Research and AI 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