@inproceedings{muti-barron-cedeno-2022-checkpoint,
title = "A Checkpoint on Multilingual Misogyny Identification",
author = "Muti, Arianna and
Barr{\'o}n-Cede{\~n}o, Alberto",
editor = "Louvan, Samuel and
Madotto, Andrea and
Madureira, Brielen",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-srw.37",
doi = "10.18653/v1/2022.acl-srw.37",
pages = "454--460",
abstract = "We address the problem of identifying misogyny in tweets in mono and multilingual settings in three languages: English, Italian, and Spanish. We explore model variations considering single and multiple languages both in the pre-training of the transformer and in the training of the downstream taskto explore the feasibility of detecting misogyny through a transfer learning approach across multiple languages. That is, we train monolingual transformers with monolingual data, and multilingual transformers with both monolingual and multilingual data. Our models reach state-of-the-art performance on all three languages. The single-language BERT models perform the best, closely followed by different configurations of multilingual BERT models. The performance drops in zero-shot classification across languages. Our error analysis shows that multilingual and monolingual models tend to make the same mistakes.",
}
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%0 Conference Proceedings
%T A Checkpoint on Multilingual Misogyny Identification
%A Muti, Arianna
%A Barrón-Cedeño, Alberto
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F muti-barron-cedeno-2022-checkpoint
%X We address the problem of identifying misogyny in tweets in mono and multilingual settings in three languages: English, Italian, and Spanish. We explore model variations considering single and multiple languages both in the pre-training of the transformer and in the training of the downstream taskto explore the feasibility of detecting misogyny through a transfer learning approach across multiple languages. That is, we train monolingual transformers with monolingual data, and multilingual transformers with both monolingual and multilingual data. Our models reach state-of-the-art performance on all three languages. The single-language BERT models perform the best, closely followed by different configurations of multilingual BERT models. The performance drops in zero-shot classification across languages. Our error analysis shows that multilingual and monolingual models tend to make the same mistakes.
%R 10.18653/v1/2022.acl-srw.37
%U https://aclanthology.org/2022.acl-srw.37
%U https://doi.org/10.18653/v1/2022.acl-srw.37
%P 454-460
Markdown (Informal)
[A Checkpoint on Multilingual Misogyny Identification](https://aclanthology.org/2022.acl-srw.37) (Muti & Barrón-Cedeño, ACL 2022)
ACL
- Arianna Muti and Alberto Barrón-Cedeño. 2022. A Checkpoint on Multilingual Misogyny Identification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 454–460, Dublin, Ireland. Association for Computational Linguistics.