@InProceedings{nejat-carenini-ng:2017:W17-55,
  author    = {Nejat, Bita  and  Carenini, Giuseppe  and  Ng, Raymond},
  title     = {Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {289--298},
  abstract  = {Discourse Parsing and Sentiment Analysis are two fundamental tasks in Natural
	Language Processing that have been shown to be mutually beneficial. In this
	work, we design and compare two Neural Based models for jointly learning both
	tasks. In the proposed approach, we first create a vector representation for
	all the text segments in the input sentence. Next, we apply three different
	Recursive Neural Net models: one for discourse structure prediction, one for
	discourse relation prediction and one for sentiment analysis. Finally, we
	combine these Neural Nets in two different joint models: Multi-tasking and
	Pre-training. Our results on two standard corpora indicate that both methods
	result in improvements in each task but Multi-tasking has a bigger impact than
	Pre-training. Specifically for Discourse Parsing, we see improvements in the
	prediction of the set of contrastive relations.},
  url       = {http://aclweb.org/anthology/W17-5535}
}