@InProceedings{wu-wang-xue:2016:COLING,
  author    = {Wu, Bowen  and  Wang, Baoxun  and  Xue, Hui},
  title     = {Ranking Responses Oriented to Conversational Relevance in Chat-bots},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {652--662},
  abstract  = {For automatic chatting systems, it is indeed a great challenge to reply the
	given query considering the conversation history, rather than based on the
	query only. This paper proposes a deep neural network to address the
	context-aware response ranking problem by end-to-end learning, so as to help to
	select conversationally relevant candidate. By combining the multi-column
	convolutional layer and the recurrent layer, our model is able to model the
	semantics of the utterance sequence by grasping the semantic clue within the
	conversation, on the basis of the effective representation for each sentence.
	Especially, the network utilizes attention pooling to further emphasis the
	importance of essential words in conversations, thus the representations of
	contexts tend to be more meaningful and the performance of candidate ranking is
	notably improved. Meanwhile, due to the adoption of attention pooling, it is
	possible to visualize the semantic clues. The experimental results on the large
	amount of conversation data from social media have shown that our approach is
	promising for quantifying the conversational relevance of responses, and
	indicated its good potential for building practical IR based chat-bots.},
  url       = {http://aclweb.org/anthology/C16-1063}
}