# Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers

arXiv: https://arxiv.org/abs/2402.17914 · HTML: https://mlsanity.com/paper/2402.17914/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control](https://mlsanity.com/paper/1910.13294/) (2019)
- [Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions](https://mlsanity.com/paper/1901.03729/) (2019)
- [SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers](https://mlsanity.com/paper/2103.12279/) (2021)
- [Automatic Language Identification in Texts: A Survey](https://mlsanity.com/paper/1804.08186/) (2018)
- [Understanding Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1809.08037/) (2018)
- [Learning to Recognize Dialect Features](https://mlsanity.com/paper/2010.12707/) (2020)
- [Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models](https://mlsanity.com/paper/1911.06194/) (2019)
- [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://mlsanity.com/paper/2210.00193/) (2022)
- [Findings of the VarDial Evaluation Campaign 2023](https://mlsanity.com/paper/2305.20080/) (2023)
- [Fine-Tuning BERT with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languages](https://mlsanity.com/paper/2303.17683/) (2023)
- [StyLEx: Explaining Style Using Human Lexical Annotations](https://mlsanity.com/paper/2210.07469/) (2022)
- [Syntax-guided Localized Self-attention by Constituency Syntactic Distance](https://mlsanity.com/paper/2210.11759/) (2022)

## Built on by (most cited first, 3 of 3)

- [Explainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling](https://mlsanity.com/paper/2404.18510/) (2024)
- [Wiktionary as a Crowdsourced Lexicon for English Dialects](https://mlsanity.com/paper/2608.15641/) (2026)
- [Right-Wing Rock or Just Rock? A Computational Linguistic Analysis of Frei.Wild](https://mlsanity.com/paper/2609.39460/) (2026)

## Abstract (arXiv)

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.