Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers
This addresses the challenge of dialect analysis for linguists and NLP researchers, but it appears incremental as it applies interpretable classifiers to a known task.
The paper tackled the problem of identifying linguistic differences between dialects without expert knowledge by using interpretable dialect classifiers, and demonstrated that the method successfully extracts key lexical features for Mandarin, Italian, and Low Saxon dialects.
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.