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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S19-2085.pdf