@InProceedings{tian-EtAl:2017:Short,
  author    = {Tian, Zhiliang  and  Yan, Rui  and  Mou, Lili  and  Song, Yiping  and  Feng, Yansong  and  Zhao, Dongyan},
  title     = {How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {231--236},
  abstract  = {Generative conversational systems are attracting increasing attention in
	natural language processing (NLP). Recently, researchers have noticed the
	importance of context information in dialog processing, and built various
	models to utilize context. However, there is no systematic comparison to
	analyze how to use context effectively. In this paper, we conduct an empirical
	study to compare various models and investigate the effect of context
	information in dialog systems. We also propose a variant that explicitly
	weights context vectors by context-query relevance, outperforming the other
	baselines.},
  url       = {http://aclweb.org/anthology/P17-2036}
}