Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers
Roy Xie, Orevaoghene Ahia, Yulia Tsvetkov, Antonios Anastasopoulos
Abstract
Identifying linguistic differences between dialects of a language often requires expert knowledge and meticulous human analysis. This is largely due to the complexity and nuance involved in studying various dialects. We present a novel approach to extract distinguishing lexical features of dialects by utilizing interpretable dialect classifiers, even in the absence of human experts. We explore both post-hoc and intrinsic approaches to interpretability, conduct experiments on Mandarin, Italian, and Low Saxon, and experimentally demonstrate that our method successfully identifies key language-specific lexical features that contribute to dialectal variations.- Anthology ID:
- 2024.naacl-short.5
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 54–69
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.5
- DOI:
- Cite (ACL):
- Roy Xie, Orevaoghene Ahia, Yulia Tsvetkov, and Antonios Anastasopoulos. 2024. Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 54–69, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers (Xie et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.naacl-short.5.pdf