@InProceedings{kumar-joshi:2016:COLING,
  author    = {Kumar, Vineet  and  Joshi, Sachindra},
  title     = {Non-sentential Question Resolution using Sequence to Sequence Learning},
  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     = {2022--2031},
  abstract  = {An interactive Question Answering (QA) system frequently encounters
	non-sentential (incomplete) questions. These non-sentential questions may not
	make sense to the system when a user asks them without the context of
	conversation. The system thus needs to take into account the conversation
	context to process the question. In this work, we present a recurrent neural
	network (RNN) based encoder decoder network that can generate a complete
	(intended) question, given an incomplete question and  conversation context.
	RNN encoder decoder networks have been show to work well when trained on a
	parallel corpus with millions of sentences, however it is extremely hard to
	obtain conversation data of this magnitude. We therefore propose to decompose
	the original problem into two separate simplified problems where each problem
	focuses on an abstraction. Specifically, we train a semantic sequence model to
	learn semantic patterns, and a syntactic sequence model to learn linguistic
	patterns. We further combine syntactic and semantic sequence models to generate
	an ensemble model. Our model achieves a BLEU score of 30.15 as compared to
	18.54 using a standard RNN encoder decoder model.},
  url       = {http://aclweb.org/anthology/C16-1190}
}