@inproceedings{baziotis-etal-2017-datastories,
    title = "{D}ata{S}tories at {S}em{E}val-2017 Task 6: {S}iamese {LSTM} with Attention for Humorous Text Comparison",
    author = "Baziotis, Christos  and
      Pelekis, Nikos  and
      Doulkeridis, Christos",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S17-2065/",
    doi = "10.18653/v1/S17-2065",
    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."
}