@InProceedings{yao-EtAl:2017:EMNLP2017,
  author    = {Yao, Lili  and  Zhang, Yaoyuan  and  Feng, Yansong  and  Zhao, Dongyan  and  Yan, Rui},
  title     = {Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2190--2199},
  abstract  = {The study on human-computer conversation systems is a hot research topic
	nowadays. One of the prevailing methods to build the system is using the
	generative Sequence-to-Sequence (Seq2Seq) model through neural networks.
	However, the standard Seq2Seq model is prone to generate trivial responses. In
	this paper, we aim to generate a more meaningful and informative reply when
	answering a given question. We propose an implicit content-introducing method
	which incorporates additional information into the Seq2Seq model in a flexible
	way. Specifically, we fuse the general decoding and the auxiliary cue word
	information through our proposed hierarchical gated fusion unit. Experiments on
	real-life data demonstrate that our model consistently outperforms a set of
	competitive baselines in terms of BLEU scores and human evaluation.},
  url       = {https://www.aclweb.org/anthology/D17-1233}
}