@inproceedings{husain-etal-2020-salamnet,
title = "{S}alam{NET} at {S}em{E}val-2020 Task 12: Deep Learning Approach for {A}rabic Offensive Language Detection",
author = "Husain, Fatemah and
Lee, Jooyeon and
Henry, Sam and
Uzuner, Ozlem",
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-1.283",
doi = "10.18653/v1/2020.semeval-1.283",
pages = "2133--2139",
abstract = "This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83{\%}",
}
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<abstract>This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83%</abstract>
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%0 Conference Proceedings
%T SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detection
%A Husain, Fatemah
%A Lee, Jooyeon
%A Henry, Sam
%A Uzuner, Ozlem
%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 husain-etal-2020-salamnet
%X This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83%
%R 10.18653/v1/2020.semeval-1.283
%U https://aclanthology.org/2020.semeval-1.283
%U https://doi.org/10.18653/v1/2020.semeval-1.283
%P 2133-2139
Markdown (Informal)
[SalamNET at SemEval-2020 Task 12: Deep Learning Approach for Arabic Offensive Language Detection](https://aclanthology.org/2020.semeval-1.283) (Husain et al., SemEval 2020)
ACL