@InProceedings{baziotis-pelekis-doulkeridis:2017:SemEval1,
  author    = {Baziotis, Christos  and  Pelekis, Nikos  and  Doulkeridis, Christos},
  title     = {DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {390--395},
  abstract  = {In this paper we present a deep-learning system that competed at SemEval-2017
	Task 6 “\#HashtagWars: Learning a Sense of Humor”. We participated in
	Subtask A, in which the goal was, given two Twitter messages, to identify which
	one is funnier. We propose a Siamese architecture with bidirectional Long
	Short-Term Memory (LSTM) networks, augmented with an attention mechanism. Our
	system works on the token-level, leveraging word embeddings trained on a big
	collection of unlabeled Twitter messages. We ranked 2nd in 7 teams. A
	post-completion improvement of our model, achieves state-of-the-art results on
	\#HashtagWars dataset.},
  url       = {http://www.aclweb.org/anthology/S17-2065}
}