DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection

Zoher Orabe, Bushr Haddad, Nada Ghneim, Anas Al-Abood

Abstract
This paper describes our team work and submission for the SemEval 2020 (Sub-Task A) “Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media”. Our two baseline models were based on different levels of representation: character vs. word level. In word level based representation we implemented a convolutional neural network model and a bi-directional GRU model. In character level based representation we implemented a hyper CNN and LSTM model. All of these models have been further augmented with attention layers for a better performance on our task. We also experimented with three types of static word embeddings: word2vec, FastText, and Glove, in addition to emoji embeddings, and compared the performance of the different deep learning models on the dataset provided by this task. The bi-directional GRU model with attention has achieved the highest score (0.85% F1 score) among all other models.
Anthology ID:
2020.semeval-1.254
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1932–1937
Language:
URL:
https://aclanthology.org/2020.semeval-1.254
DOI:
10.18653/v1/2020.semeval-1.254
Bibkey:
Cite (ACL):
Zoher Orabe, Bushr Haddad, Nada Ghneim, and Anas Al-Abood. 2020. DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1932–1937, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection (Orabe et al., SemEval 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-3/2020.semeval-1.254.pdf
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