# Learning to Recognize Dialect Features

arXiv: https://arxiv.org/abs/2010.12707 · HTML: https://mlsanity.com/paper/2010.12707/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 29 of 29)

- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [Re-contextualizing Fairness in NLP: The Case of India](https://mlsanity.com/paper/2209.12226/) (2022)
- [Multi-VALUE: A Framework for Cross-Dialectal English NLP](https://mlsanity.com/paper/2212.08011/) (2022)
- [VALUE: Understanding Dialect Disparity in NLU](https://mlsanity.com/paper/2204.03031/) (2022)
- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [Evaluation of African American Language Bias in Natural Language Generation](https://mlsanity.com/paper/2305.14291/) (2023)
- [The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative](https://mlsanity.com/paper/2210.13181/) (2022)
- [Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models](https://mlsanity.com/paper/2306.02080/) (2023)
- [Efficient Test Time Adapter Ensembling for Low-resource Language Varieties](https://mlsanity.com/paper/2109.04877/) (2021)
- [ALDi: Quantifying the Arabic Level of Dialectness of Text](https://mlsanity.com/paper/2310.13747/) (2023)
- [Robustness Analysis of Video-Language Models Against Visual and Language Perturbations](https://mlsanity.com/paper/2207.02159/) (2022)
- [TADA: Task-Agnostic Dialect Adapters for English](https://mlsanity.com/paper/2305.16651/) (2023)
- [Geographic Adaptation of Pretrained Language Models](https://mlsanity.com/paper/2203.08565/) (2022)
- [Dialect-robust Evaluation of Generated Text](https://mlsanity.com/paper/2211.00922/) (2022)
- [Evaluating Dialect Robustness of Language Models via Conversation Understanding](https://mlsanity.com/paper/2405.05688/) (2024)
- [MD3: The Multi-Dialect Dataset of Dialogues](https://mlsanity.com/paper/2305.11355/) (2023)
- [DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules](https://mlsanity.com/paper/2305.13406/) (2023)
- [Evaluating Decision Models for Text Annotation in Computational Social Science](https://mlsanity.com/paper/2609.24574/) (2026)
- [Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties](https://mlsanity.com/paper/2209.07611/) (2022)
- [Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Models](https://mlsanity.com/paper/2409.00358/) (2024)
- [Social Meme-ing: Measuring Linguistic Variation in Memes](https://mlsanity.com/paper/2311.09130/) (2023)
- [On the Universality of Deep Contextual Language Models](https://mlsanity.com/paper/2109.07140/) (2021)
- [Epicurus at SemEval-2023 Task 4: Improving Prediction of Human Values behind Arguments by Leveraging Their Definitions](https://mlsanity.com/paper/2302.13925/) (2023)
- [Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties](https://mlsanity.com/paper/2402.02078/) (2024)
- [Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers](https://mlsanity.com/paper/2402.17914/) (2024)
- [How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline](https://mlsanity.com/paper/2604.04204/) (2026)
- [Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages](https://mlsanity.com/paper/2203.09632/) (2022)
- [Lost in Variation? Evaluating NLI Performance in Basque and Spanish Geographical Variants](https://mlsanity.com/paper/2506.15239/) (2025)
- [Dialetto, ma Quanto Dialetto? Transcribing and Evaluating Dialects on a Continuum](https://mlsanity.com/paper/2410.14589/) (2024)

## Abstract (arXiv)

Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in "He {} running". In this paper, we introduce the task of dialect feature detection, and present two multitask learning approaches, both based on pretrained transformers. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. We train our models on a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples. We also demonstrate the downstream applicability of dialect feature detection both as a measure of dialect density and as a dialect classifier.