SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing
Arturo Montejo-Ráez, Salud María Jiménez-Zafra, Miguel A. García-Cumbreras, Manuel Carlos Díaz-Galiano
Abstract
This paper describes the participation of the SINAI-DL team at Task 5 in SemEval 2019, called HatEval. We have applied some classic neural network layers, like word embeddings and LSTM, to build a neural classifier for both proposed tasks. Due to the small amount of training data provided compared to what is expected for an adequate learning stage in deep architectures, we explore the use of paraphrasing tools as source for data augmentation. Our results show that this method is promising, as some improvement has been found over non-augmented training sets.- Anthology ID:
- S19-2085
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Editors:
- Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 480–483
- Language:
- URL:
- https://aclanthology.org/S19-2085
- DOI:
- 10.18653/v1/S19-2085
- Cite (ACL):
- Arturo Montejo-Ráez, Salud María Jiménez-Zafra, Miguel A. García-Cumbreras, and Manuel Carlos Díaz-Galiano. 2019. SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 480–483, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing (Montejo-Ráez et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/S19-2085.pdf