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        <title>“Is Hate Lost in Translation?”: Evaluation of Multilingual LGBTQIA+ Hate Speech Detection</title>
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    <abstract>This paper explores the challenges of detecting LGBTQIA+ hate speech of large language models across multiple languages, including English, Italian, Chinese and (code-mixed) English-Tamil, examining the impact of machine translation and whether the nuances of hate speech are preserved across translation. We examine the hate speech detection ability of zero-shot and fine-tuned GPT. Our findings indicate that: (1) English has the highest performance and the code-mixing scenario of English-Tamil being the lowest, (2) fine-tuning improves performance consistently across languages whilst translation yields mixed results. Through simple experimentation with original text and machine-translated text for hate speech detection along with a qualitative error analysis, this paper sheds light on the socio-cultural nuances and complexities of languages that may not be captured by automatic translation.</abstract>
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%0 Conference Proceedings
%T “Is Hate Lost in Translation?”: Evaluation of Multilingual LGBTQIA+ Hate Speech Detection
%A Chan, Fai Leui
%A Nguyen, Duke
%A Joshi, Aditya
%Y Baldwin, Tim
%Y Rodríguez Méndez, Sergio José
%Y Kuo, Nicholas
%S Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association
%D 2024
%8 December
%I Association for Computational Linguistics
%C Canberra, Australia
%F chan-etal-2024-hate
%X This paper explores the challenges of detecting LGBTQIA+ hate speech of large language models across multiple languages, including English, Italian, Chinese and (code-mixed) English-Tamil, examining the impact of machine translation and whether the nuances of hate speech are preserved across translation. We examine the hate speech detection ability of zero-shot and fine-tuned GPT. Our findings indicate that: (1) English has the highest performance and the code-mixing scenario of English-Tamil being the lowest, (2) fine-tuning improves performance consistently across languages whilst translation yields mixed results. Through simple experimentation with original text and machine-translated text for hate speech detection along with a qualitative error analysis, this paper sheds light on the socio-cultural nuances and complexities of languages that may not be captured by automatic translation.
%U https://aclanthology.org/2024.alta-1.11/
%P 146-152

Markdown (Informal)

[“Is Hate Lost in Translation?”: Evaluation of Multilingual LGBTQIA+ Hate Speech Detection](https://aclanthology.org/2024.alta-1.11/) (Chan et al., ALTA 2024)

• “Is Hate Lost in Translation?”: Evaluation of Multilingual LGBTQIA+ Hate Speech Detection (Chan et al., ALTA 2024)

ACL

• Fai Leui Chan, Duke Nguyen, and Aditya Joshi. 2024. “Is Hate Lost in Translation?”: Evaluation of Multilingual LGBTQIA+ Hate Speech Detection. In Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association, pages 146–152, Canberra, Australia. Association for Computational Linguistics.