@InProceedings{khanpour-guntakandla-nielsen:2016:COLING,
  author    = {Khanpour, Hamed  and  Guntakandla, Nishitha  and  Nielsen, Rodney},
  title     = {Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network},
  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     = {2012--2021},
  abstract  = {In this study, we applied a deep LSTM structure to classify dialogue acts (DAs)
	in open-domain conversations. We found that the word embeddings parameters,
	dropout regularization, decay rate and number of layers are the parameters that
	have the largest effect on the final system accuracy. Using the findings of
	these experiments, we trained a deep LSTM network that outperforms the
	state-of-the-art on the Switchboard corpus by 3.11\%, and MRDA by 2.2\%.},
  url       = {http://aclweb.org/anthology/C16-1189}
}