@inproceedings{orabe-etal-2020-dothemath,
title = "{D}o{T}he{M}ath at {S}em{E}val-2020 Task 12 : Deep Neural Networks with Self Attention for {A}rabic Offensive Language Detection",
author = "Orabe, Zoher and
Haddad, Bushr and
Ghneim, Nada and
Al-Abood, Anas",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.254",
doi = "10.18653/v1/2020.semeval-1.254",
pages = "1932--1937",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection
%A Orabe, Zoher
%A Haddad, Bushr
%A Ghneim, Nada
%A Al-Abood, Anas
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F orabe-etal-2020-dothemath
%X 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.
%R 10.18653/v1/2020.semeval-1.254
%U https://aclanthology.org/2020.semeval-1.254
%U https://doi.org/10.18653/v1/2020.semeval-1.254
%P 1932-1937
Markdown (Informal)
[DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection](https://aclanthology.org/2020.semeval-1.254) (Orabe et al., SemEval 2020)
ACL