Nozza@LT-EDI-ACL2022: Ensemble Modeling for Homophobia and Transphobia Detection

Debora Nozza


Abstract
In this paper, we describe our approach for the task of homophobia and transphobia detection in English social media comments. The dataset consists of YouTube comments, and it has been released for the shared task on Homophobia/Transphobia Detection in social media comments. Given the high class imbalance, we propose a solution based on data augmentation and ensemble modeling. We fine-tuned different large language models (BERT, RoBERTa, and HateBERT) and used the weighted majority vote on their predictions. Our proposed model obtained 0.48 and 0.94 for macro and weighted F1-score, respectively, ranking at the third position.
Anthology ID:
2022.ltedi-1.37
Volume:
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
Venue:
LTEDI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
258–264
Language:
URL:
https://aclanthology.org/2022.ltedi-1.37
DOI:
10.18653/v1/2022.ltedi-1.37
Bibkey:
Cite (ACL):
Debora Nozza. 2022. Nozza@LT-EDI-ACL2022: Ensemble Modeling for Homophobia and Transphobia Detection. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 258–264, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Nozza@LT-EDI-ACL2022: Ensemble Modeling for Homophobia and Transphobia Detection (Nozza, LTEDI 2022)
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PDF:
https://aclanthology.org/2022.ltedi-1.37.pdf
Video:
 https://aclanthology.org/2022.ltedi-1.37.mp4