cantnlp@LT-EDI-2024: Automatic Detection of Anti-LGBTQ+ Hate Speech in Under-resourced Languages
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Abstract (arXiv)
This paper describes our homophobia/transphobia in social media comments detection system developed as part of the shared task at LT-EDI-2024. We took a transformer-based approach to develop our multiclass classification model for ten language conditions (English, Spanish, Gujarati, Hindi, Kannada, Malayalam, Marathi, Tamil, Tulu, and Telugu). We introduced synthetic and organic instances of script-switched language data during domain adaptation to mirror the linguistic realities of social media language as seen in the labelled training data. Our system ranked second for Gujarati and Telugu with varying levels of performance for other language conditions. The results suggest incorporating elements of paralinguistic behaviour such as script-switching may improve the performance of language detection systems especially in the cases of under-resourced languages conditions.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2020Mapping Languages: The Corpus of Global Language Usecited by 42
- 2020Investigating Gender Bias in BERTcited by 40
- 2021Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Commentscited by 20
- 2023cantnlp@LT-EDI-2023: Homophobia/Transphobia Detection in Social Media Comments using Spatio-Temporally Retrained Language Modelscited by 3
- 2023Comparing Measures of Linguistic Diversity Across Social Media Language Data and Census Data at Subnational Geographic Areascited by 1
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