@inproceedings{henia-etal-2021-icompass,
title = "i{C}ompass at {NLP}4{IF}-2021{--}Fighting the {COVID}-19 Infodemic",
author = "Henia, Wassim and
Rjab, Oumayma and
Haddad, Hatem and
Fourati, Chayma",
editor = "Feldman, Anna and
Da San Martino, Giovanni and
Leberknight, Chris and
Nakov, Preslav",
booktitle = "Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4if-1.17/",
doi = "10.18653/v1/2021.nlp4if-1.17",
pages = "115--118",
abstract = "This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-the-art contextualized text representation models that were fine-tuned for the downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set."
}
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<abstract>This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-the-art contextualized text representation models that were fine-tuned for the downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set.</abstract>
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%0 Conference Proceedings
%T iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic
%A Henia, Wassim
%A Rjab, Oumayma
%A Haddad, Hatem
%A Fourati, Chayma
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F henia-etal-2021-icompass
%X This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-the-art contextualized text representation models that were fine-tuned for the downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set.
%R 10.18653/v1/2021.nlp4if-1.17
%U https://aclanthology.org/2021.nlp4if-1.17/
%U https://doi.org/10.18653/v1/2021.nlp4if-1.17
%P 115-118
Markdown (Informal)
[iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic](https://aclanthology.org/2021.nlp4if-1.17/) (Henia et al., NLP4IF 2021)
ACL
- Wassim Henia, Oumayma Rjab, Hatem Haddad, and Chayma Fourati. 2021. iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 115–118, Online. Association for Computational Linguistics.