Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers

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.

Publication
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)
Ruoyu (Roy) Xie
Ruoyu (Roy) Xie
Undergraduate → PhD@Duke CS
Antonios Anastasopoulos
Antonios Anastasopoulos
Assistant Professor

I work on multilingual models, machine translation, speech recognition, and NLP for under-served languages.

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