@InProceedings{wu-EtAl:2017:Long1,
  author    = {Wu, Yu  and  Wu, Wei  and  Xing, Chen  and  Zhou, Ming  and  Li, Zhoujun},
  title     = {Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {496--505},
  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 the utterances or important information in the context. 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 the utterances. The final matching score is
	calculated with the hidden states of the RNN. 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.},
  url       = {http://aclweb.org/anthology/P17-1046}
}