@InProceedings{he-EtAl:2017:Long1,
  author    = {He, Shizhu  and  Liu, Cao  and  Liu, Kang  and  Zhao, Jun},
  title     = {Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning},
  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     = {199--208},
  abstract  = {Generating answer with natural language sentence is very important in
	real-world question answering systems, which needs to obtain a right answer as
	well as a coherent natural response. In this paper, we propose an end-to-end
	question answering system called COREQA in sequence-to-sequence learning, which
	incorporates copying and retrieving mechanisms to generate natural answers
	within an encoder-decoder framework. Specifically, in COREQA, the semantic
	units (words, phrases and entities) in a natural answer are dynamically
	predicted from the vocabulary, copied from the given question and/or retrieved
	from the corresponding knowledge base jointly. Our empirical study on both
	synthetic and real-world datasets demonstrates the efficiency of COREQA, which
	is able to generate correct, coherent and natural answers for knowledge
	inquired questions.},
  url       = {http://aclweb.org/anthology/P17-1019}
}