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.- Anthology ID:
- S17-2065
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 390–395
- Language:
- URL:
- https://aclanthology.org/S17-2065
- DOI:
- 10.18653/v1/S17-2065
- Cite (ACL):
- Christos Baziotis, Nikos Pelekis, and Christos Doulkeridis. 2017. DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 390–395, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison (Baziotis et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/S17-2065.pdf