@InProceedings{li-EtAl:2016:COLING6,
  author    = {Li, Chaozhuo  and  Wu, Yu  and  Wu, Wei  and  Xing, Chen  and  Li, Zhoujun  and  Zhou, Ming},
  title     = {Detecting Context Dependent Messages in a Conversational Environment},
  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     = {1990--1999},
  abstract  = {While automatic response generation for building chatbot systems has drawn a
	lot of attention recently, there is limited understanding on when we need to
	consider the linguistic context of an input text in the generation process. The
	task is challenging, as messages in a conversational environment are short and
	informal, and evidence that can indicate a message is context dependent is
	scarce.
	After a study of social conversation data crawled from the web, we observed
	that some characteristics estimated from the responses of messages are
	discriminative for identifying context dependent messages.
	With the characteristics as weak supervision, we propose using a Long Short
	Term Memory (LSTM) network to learn a classifier.  Our method carries out text
	representation and classifier learning in a unified framework.                       
	     
	Experimental
	results show that the proposed method can significantly outperform baseline
	methods on accuracy of classification.},
  url       = {http://aclweb.org/anthology/C16-1187}
}