@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison
%A Baziotis, Christos
%A Pelekis, Nikos
%A Doulkeridis, Christos
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F baziotis-etal-2017-datastories
%X 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.
%R 10.18653/v1/S17-2065
%U https://aclanthology.org/S17-2065
%U https://doi.org/10.18653/v1/S17-2065
%P 390-395
Markdown (Informal)
[DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison](https://aclanthology.org/S17-2065) (Baziotis et al., SemEval 2017)
ACL