@InProceedings{bugert-EtAl:2017:LSDSem,
  author    = {Bugert, Michael  and  Puzikov, Yevgeniy  and  R\"{u}ckl\'{e}, Andreas  and  Eckle-Kohler, Judith  and  Martin, Teresa  and  Mart\'{i}nez-C\'{a}mara, Eugenio  and  Sorokin, Daniil  and  Peyrard, Maxime  and  Gurevych, Iryna},
  title     = {LSDSem 2017: Exploring Data Generation Methods 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     = {56--61},
  abstract  = {The Story Cloze test is a recent effort in providing a common test scenario for
	text understanding systems.
	As part of the LSDSem 2017 shared task, we present a system based on a deep
	learning architecture combined with a rich set of manually-crafted linguistic
	features. The system outperforms all known baselines for the task, suggesting
	that the chosen approach is promising. We additionally present two methods for
	generating further training data based on stories from the ROCStories corpus.},
  url       = {http://aclweb.org/anthology/W17-0908}
}