@inproceedings{hassan-etal-2020-alt-semeval,
title = "{ALT} at {S}em{E}val-2020 Task 12: {A}rabic and {E}nglish Offensive Language Identification in Social Media",
author = "Hassan, Sabit and
Samih, Younes and
Mubarak, Hamdy and
Abdelali, Ahmed",
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-1.249",
doi = "10.18653/v1/2020.semeval-1.249",
pages = "1891--1897",
abstract = "This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90{\%} macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.",
}
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<abstract>This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90% macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.</abstract>
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%0 Conference Proceedings
%T ALT at SemEval-2020 Task 12: Arabic and English Offensive Language Identification in Social Media
%A Hassan, Sabit
%A Samih, Younes
%A Mubarak, Hamdy
%A Abdelali, Ahmed
%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 hassan-etal-2020-alt-semeval
%X This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90% macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.
%R 10.18653/v1/2020.semeval-1.249
%U https://aclanthology.org/2020.semeval-1.249
%U https://doi.org/10.18653/v1/2020.semeval-1.249
%P 1891-1897
Markdown (Informal)
[ALT at SemEval-2020 Task 12: Arabic and English Offensive Language Identification in Social Media](https://aclanthology.org/2020.semeval-1.249) (Hassan et al., SemEval 2020)
ACL