Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Cultural Compass : Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features. / Zhou, Li; Karamolegkou, Antonia; Chen, Wenyu ; Hershcovich, Daniel.

Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. p. 12684–12702.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Zhou, L, Karamolegkou, A, Chen, W & Hershcovich, D 2023, Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features. in Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), pp. 12684–12702, 2023 Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, 06/12/2023. <https://aclanthology.org/2023.findings-emnlp.845/>

APA

Zhou, L., Karamolegkou, A., Chen, W., & Hershcovich, D. (2023). Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 12684–12702). Association for Computational Linguistics (ACL). https://aclanthology.org/2023.findings-emnlp.845/

Vancouver

Zhou L, Karamolegkou A, Chen W, Hershcovich D. Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features. In Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL). 2023. p. 12684–12702

Author

Zhou, Li ; Karamolegkou, Antonia ; Chen, Wenyu ; Hershcovich, Daniel. / Cultural Compass : Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features. Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), 2023. pp. 12684–12702

Bibtex

@inproceedings{043a0fa0a59b44b299627925cff48e23,
title = "Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features",
abstract = "The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, however, a notable gap exists in determining if cultural features can accurately predict the success of cross-cultural transfer learning for such subjective tasks. Addressing this, our study delves into the intersection of cultural features and transfer learning effectiveness. The findings reveal that cultural value surveys indeed possess a predictive power for cross-cultural transfer learning success in OLD tasks, and that it can be further improved using offensive word distance. Based on these results, we advocate for the integration of cultural information into datasets. Additionally, we recommend leveraging data sources rich in cultural information, such as surveys, to enhance cultural adaptability. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.",
author = "Li Zhou and Antonia Karamolegkou and Wenyu Chen and Daniel Hershcovich",
year = "2023",
language = "English",
isbn = "979-8-89176-061-5",
pages = "12684–12702",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
publisher = "Association for Computational Linguistics (ACL)",
address = "United States",
note = "2023 Findings of the Association for Computational Linguistics: EMNLP 2023 ; Conference date: 06-12-2023 Through 10-12-2023",

}

RIS

TY - GEN

T1 - Cultural Compass

T2 - 2023 Findings of the Association for Computational Linguistics: EMNLP 2023

AU - Zhou, Li

AU - Karamolegkou, Antonia

AU - Chen, Wenyu

AU - Hershcovich, Daniel

PY - 2023

Y1 - 2023

N2 - The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, however, a notable gap exists in determining if cultural features can accurately predict the success of cross-cultural transfer learning for such subjective tasks. Addressing this, our study delves into the intersection of cultural features and transfer learning effectiveness. The findings reveal that cultural value surveys indeed possess a predictive power for cross-cultural transfer learning success in OLD tasks, and that it can be further improved using offensive word distance. Based on these results, we advocate for the integration of cultural information into datasets. Additionally, we recommend leveraging data sources rich in cultural information, such as surveys, to enhance cultural adaptability. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.

AB - The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, however, a notable gap exists in determining if cultural features can accurately predict the success of cross-cultural transfer learning for such subjective tasks. Addressing this, our study delves into the intersection of cultural features and transfer learning effectiveness. The findings reveal that cultural value surveys indeed possess a predictive power for cross-cultural transfer learning success in OLD tasks, and that it can be further improved using offensive word distance. Based on these results, we advocate for the integration of cultural information into datasets. Additionally, we recommend leveraging data sources rich in cultural information, such as surveys, to enhance cultural adaptability. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.

M3 - Article in proceedings

SN - 979-8-89176-061-5

SP - 12684

EP - 12702

BT - Findings of the Association for Computational Linguistics: EMNLP 2023

PB - Association for Computational Linguistics (ACL)

Y2 - 6 December 2023 through 10 December 2023

ER -

ID: 381796912