Details
| Original language | English |
|---|---|
| Title of host publication | Findings of the Association for Computational Linguistics ACL 2024 |
| Editors | Lun-Wei Ku, Andre Martins, Vivek Srikumar |
| Pages | 9294-9313 |
| Number of pages | 20 |
| ISBN (electronic) | 9798891760998 |
| Publication status | Published - Aug 2024 |
| Event | Findings of the Association for Computational Linguistics ACL 2024 - Bangkok, Thailand Duration: 11 Aug 2024 → 16 Aug 2024 https://2024.aclweb.org/ |
Publication series
| Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
|---|---|
| ISSN (Print) | 0736-587X |
Abstract
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Linguistics and Language
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Findings of the Association for Computational Linguistics ACL 2024. ed. / Lun-Wei Ku; Andre Martins; Vivek Srikumar. 2024. p. 9294-9313 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
AU - Spliethöver, Maximilian
AU - Menon, Sai Nikhil
AU - Wachsmuth, Henning
N1 - Publisher Copyright: © 2024 Association for Computational Linguistics.
PY - 2024/8
Y1 - 2024/8
N2 - Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.
AB - Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.
UR - http://www.scopus.com/inward/record.url?scp=85205286976&partnerID=8YFLogxK
U2 - 10.18653/v1/2024.findings-acl.553
DO - 10.18653/v1/2024.findings-acl.553
M3 - Conference contribution
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 9294
EP - 9313
BT - Findings of the Association for Computational Linguistics ACL 2024
A2 - Ku, Lun-Wei
A2 - Martins, Andre
A2 - Srikumar, Vivek
T2 - Findings of the Association for Computational Linguistics ACL 2024
Y2 - 11 August 2024 through 16 August 2024
ER -