@InProceedings{roemmele-EtAl:2017:LSDSem,
  author    = {Roemmele, Melissa  and  Kobayashi, Sosuke  and  Inoue, Naoya  and  Gordon, Andrew},
  title     = {An RNN-based Binary Classifier for the Story Cloze Test},
  booktitle = {Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {74--80},
  abstract  = {The Story Cloze Test consists of choosing a sentence that best completes a
	story given two choices. In this paper we present a system that performs this
	task using a supervised binary classifier on top of a recurrent neural network
	to predict the probability that a given story ending is correct. The classifier
	is trained to distinguish correct story endings given in the training data from
	incorrect ones that we artificially generate. Our experiments evaluate
	different methods for generating these negative examples, as well as different
	embedding-based representations of the stories. Our best result obtains 67.2%
	accuracy on the test set, outperforming the existing top baseline of 58.5%.},
  url       = {http://aclweb.org/anthology/W17-0911}
}