MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments
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Abstract (arXiv)
In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 26 Jan 2024.
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6 of 27 references.
References · 6
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2022L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resourcescited by 49
- 2022L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languagescited by 35
- 2021Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Commentscited by 20
- 2021Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Languagecited by 15
- 2023nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla Sentiment Analysiscited by 2
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Citing papers per year: 2024: 2.
2 papers.