@InProceedings{hu-walker:2017:W17-55,
  author    = {Hu, Zhichao  and  Walker, Marilyn},
  title     = {Inferring Narrative Causality between Event Pairs in Films},
  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     = {342--351},
  abstract  = {To understand narrative, humans draw inferences about the underlying relations
	between narrative events.  Cognitive theories of narrative understanding define
	these inferences as four different types of causality, that include pairs of
	events A, B where A physically causes B (X drop, X break), to pairs of events
	where A causes emotional state B (Y saw X, Y felt fear). Previous work on
	learning narrative relations from text has either focused on "strict"
	physical causality, or has been vague about what relation is being learned.
	This paper learns pairs of causal events from a corpus of film scene
	descriptions which are action rich and tend to be told in chronological order. 
	We show that event pairs induced using our methods are of high quality and are
	judged to have a stronger causal relation than event pairs from Rel-Grams.},
  url       = {http://aclweb.org/anthology/W17-5540}
}