@InProceedings{jo-EtAl:2017:EMNLP2017,
  author    = {Jo, Yohan  and  Yoder, Michael  and  Jang, Hyeju  and  Rose, Carolyn},
  title     = {Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences},
  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     = {2179--2189},
  abstract  = {We present an unsupervised model of dialogue act sequences in conversation. By
	modeling topical themes as transitioning more slowly than dialogue acts in
	conversation, our model de-emphasizes content-related words in order to focus
	on conversational function words that signal dialogue acts. We also incorporate
	speaker tendencies to use some acts more than others as an additional predictor
	of dialogue act prevalence beyond temporal dependencies. According to the
	evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat
	corpus, the effectiveness of each modeling assumption is found to vary
	depending on characteristics of the data. De-emphasizing content-related words
	yields improvement on the CNET corpus, while utilizing speaker tendencies is
	advantageous on the NPS corpus. The components of our model complement one
	another to achieve robust performance on both corpora and outperform
	state-of-the-art baseline models.},
  url       = {https://www.aclweb.org/anthology/D17-1232}
}