@InProceedings{li-wu:2016:COLING,
  author    = {Li, Wei  and  Wu, Yunfang},
  title     = {Multi-level Gated Recurrent Neural Network for dialog act classification},
  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     = {1970--1979},
  abstract  = {In this paper we focus on the problem of dialog act (DA) labelling. This
	problem has recently attracted a lot of attention as it is an important
	sub-part of an automatic question answering system, which is currently in great
	demand. Traditional methods tend to see this problem as a sequence labelling
	task and deals with it by applying classifiers with rich features. Most of the
	current neural network models still omit the sequential information in the
	conversation.  Henceforth, we apply a novel multi-level gated recurrent neural
	network (GRNN) with non-textual information to predict the DA tag. Our model
	not only utilizes textual information, but also makes use of non-textual and
	contextual information.
	In comparison, our model has shown significant improvement over previous works
	on Switchboard Dialog Act (SWDA) task by over 6%.},
  url       = {http://aclweb.org/anthology/C16-1185}
}