@InProceedings{chaturvedi-peng-roth:2017:EMNLP2017,
  author    = {Chaturvedi, Snigdha  and  Peng, Haoruo  and  Roth, Dan},
  title     = {Story Comprehension for Predicting What Happens Next},
  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     = {1603--1614},
  abstract  = {Automatic story comprehension is a fundamental challenge in Natural Language
	Understanding, and can enable computers to learn about social norms, human
	behavior and commonsense. In this paper, we present a story comprehension model
	that explores three distinct semantic aspects: (i) the sequence of events
	described in the story, (ii) its emotional trajectory, and (iii) its plot
	consistency. We judge the model's understanding of real-world stories by
	inquiring if, like humans, it can develop an expectation of what will happen
	next in a given story. Specifically, we use it to predict the correct ending of
	a given short story from possible alternatives. The model uses a hidden
	variable to weigh the semantic aspects in the context of the story. Our
	experiments demonstrate the potential of our approach to characterize these
	semantic aspects, and the strength of the hidden variable based approach. The
	model outperforms the state-of-the-art approaches and achieves best results on
	a publicly available dataset.},
  url       = {https://www.aclweb.org/anthology/D17-1168}
}