@inproceedings{bokaei-etal-2025-culture,
title = "Culture Matters in Toxic Language Detection in {P}ersian",
author = "Bokaei, Zahra and
Magdy, Walid and
Webber, Bonnie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.456/",
doi = "10.18653/v1/2025.acl-long.456",
pages = "9290--9304",
ISBN = "979-8-89176-251-0",
abstract = "Toxic language detection is crucial for creating safer online environments and limiting the spread of harmful content. While toxic language detection has been under-explored in Persian, the current work compares different methods for this task, including fine-tuning, data enrichment, zero-shot and few-shot learning, and cross-lingual transfer learning. What is especially compelling is the impact of cultural context on transfer learning for this task: We show that the language of a country with cultural similarities to Persian yields better results in transfer learning. Conversely, the improvement is lower when the language comes from a culturally distinct country."
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<abstract>Toxic language detection is crucial for creating safer online environments and limiting the spread of harmful content. While toxic language detection has been under-explored in Persian, the current work compares different methods for this task, including fine-tuning, data enrichment, zero-shot and few-shot learning, and cross-lingual transfer learning. What is especially compelling is the impact of cultural context on transfer learning for this task: We show that the language of a country with cultural similarities to Persian yields better results in transfer learning. Conversely, the improvement is lower when the language comes from a culturally distinct country.</abstract>
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%0 Conference Proceedings
%T Culture Matters in Toxic Language Detection in Persian
%A Bokaei, Zahra
%A Magdy, Walid
%A Webber, Bonnie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F bokaei-etal-2025-culture
%X Toxic language detection is crucial for creating safer online environments and limiting the spread of harmful content. While toxic language detection has been under-explored in Persian, the current work compares different methods for this task, including fine-tuning, data enrichment, zero-shot and few-shot learning, and cross-lingual transfer learning. What is especially compelling is the impact of cultural context on transfer learning for this task: We show that the language of a country with cultural similarities to Persian yields better results in transfer learning. Conversely, the improvement is lower when the language comes from a culturally distinct country.
%R 10.18653/v1/2025.acl-long.456
%U https://aclanthology.org/2025.acl-long.456/
%U https://doi.org/10.18653/v1/2025.acl-long.456
%P 9290-9304
Markdown (Informal)
[Culture Matters in Toxic Language Detection in Persian](https://aclanthology.org/2025.acl-long.456/) (Bokaei et al., ACL 2025)
ACL
- Zahra Bokaei, Walid Magdy, and Bonnie Webber. 2025. Culture Matters in Toxic Language Detection in Persian. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9290–9304, Vienna, Austria. Association for Computational Linguistics.