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    <abstract>Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.</abstract>
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%0 Conference Proceedings
%T Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
%A Zhou, Li
%A Cabello, Laura
%A Cao, Yong
%A Hershcovich, Daniel
%Y Dev, Sunipa
%Y Prabhakaran, Vinodkumar
%Y Adelani, David Ifeoluwa
%Y Hovy, Dirk
%Y Benotti, Luciana
%S Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F zhou-etal-2023-cross
%X Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.
%R 10.18653/v1/2023.c3nlp-1.2
%U https://aclanthology.org/2023.c3nlp-1.2/
%U https://doi.org/10.18653/v1/2023.c3nlp-1.2
%P 8-15

Markdown (Informal)

[Cross-Cultural Transfer Learning for Chinese Offensive Language Detection](https://aclanthology.org/2023.c3nlp-1.2/) (Zhou et al., C3NLP 2023)

• Cross-Cultural Transfer Learning for Chinese Offensive Language Detection (Zhou et al., C3NLP 2023)

ACL

• Li Zhou, Laura Cabello, Yong Cao, and Daniel Hershcovich. 2023. Cross-Cultural Transfer Learning for Chinese Offensive Language Detection. In Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP), pages 8–15, Dubrovnik, Croatia. Association for Computational Linguistics.