Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Cross-Cultural Transfer Learning for Chinese Offensive Language Detection. / Zhou, Li; Cabello, Laura; Cao, Yong; Hershcovich, Daniel.
EACL 2023 - Cross-Cultural Considerations in NLP @ EACL, Proceedings of the Workshop. Association for Computational Linguistics (ACL), 2023. s. 8-15.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
AU - Zhou, Li
AU - Cabello, Laura
AU - Cao, Yong
AU - Hershcovich, Daniel
N1 - Publisher Copyright: © 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85174993994&partnerID=8YFLogxK
U2 - 10.18653/v1/2023.c3nlp-1.2
DO - 10.18653/v1/2023.c3nlp-1.2
M3 - Article in proceedings
AN - SCOPUS:85174993994
SP - 8
EP - 15
BT - EACL 2023 - Cross-Cultural Considerations in NLP @ EACL, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 1st Workshop on Cross-Cultural Considerations in NLP, C3NLP 2023
Y2 - 5 May 2023
ER -
ID: 372613556