Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers

TL;DR

This paper introduces a novel method for extracting lexical features that distinguish dialects using interpretable classifiers, reducing reliance on expert analysis and effectively identifying key dialect-specific words.

Contribution

The paper presents a new approach combining post-hoc and intrinsic interpretability techniques to automatically identify lexical features of dialects without expert intervention.

Findings

Successfully identified key lexical features in Mandarin, Italian, and Low Saxon dialects.

Demonstrated effectiveness of interpretable classifiers in dialect feature extraction.

Validated approach through experimental analysis on multiple languages.

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.