@InProceedings{mihaylov-frank:2017:LSDSem,
  author    = {Mihaylov, Todor  and  Frank, Anette},
  title     = {Story Cloze Ending Selection Baselines and Data Examination},
  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     = {87--92},
  abstract  = {This paper describes two supervised baseline systems for the Story Cloze Test
	Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
	features based on word embeddings and semantic similarity computation. We
	further implement a neural LSTM system with different encoding strategies that
	try to model the relation between the story and the
	provided endings. Our experiments show that a model using representation
	features based on average word embedding vectors over the given story words and
	the candidate ending sentences words, joint with similarity features between
	the story and candidate ending representations performed better than the neural
	models. Our best model based on achieves an accuracy
	of 72.42, ranking 3rd in the official evaluation.},
  url       = {http://aclweb.org/anthology/W17-0913}
}