@inproceedings{montejo-raez-etal-2019-sinai,
title = "{SINAI}-{DL} at {S}em{E}val-2019 Task 5: Recurrent networks and data augmentation by paraphrasing",
author = "Montejo-R{\'a}ez, Arturo and
Jim{\'e}nez-Zafra, Salud Mar{\'\i}a and
Garc{\'\i}a-Cumbreras, Miguel A. and
D{\'\i}az-Galiano, Manuel Carlos",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2085",
doi = "10.18653/v1/S19-2085",
pages = "480--483",
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.",
}
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%0 Conference Proceedings
%T SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing
%A Montejo-Ráez, Arturo
%A Jiménez-Zafra, Salud María
%A García-Cumbreras, Miguel A.
%A Díaz-Galiano, Manuel Carlos
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F montejo-raez-etal-2019-sinai
%X 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.
%R 10.18653/v1/S19-2085
%U https://aclanthology.org/S19-2085
%U https://doi.org/10.18653/v1/S19-2085
%P 480-483
Markdown (Informal)
[SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing](https://aclanthology.org/S19-2085) (Montejo-Ráez et al., SemEval 2019)
ACL