@InProceedings{tran-zukerman-haffari:2017:EMNLP2017,
  author    = {Tran, Quan Hung  and  Zukerman, Ingrid  and  Haffari, Gholamreza},
  title     = {Preserving Distributional Information in Dialogue Act Classification},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2151--2156},
  abstract  = {This paper introduces a novel training/decoding strategy for sequence labeling.
	Instead of greedily choosing a label at each time step, and using it for the
	next prediction, we retain the probability distribution over the current label,
	and pass this distribution to the next prediction. This approach allows us to
	avoid the effect of label bias and error propagation in sequence
	learning/decoding. Our experiments on dialogue act classification demonstrate
	the effectiveness of this approach. Even though our underlying neural network
	model is relatively simple, it outperforms more complex neural models,
	achieving state-of-the-art results on the MapTask and Switchboard corpora.},
  url       = {https://www.aclweb.org/anthology/D17-1229}
}