# Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties

arXiv: https://arxiv.org/abs/2209.07611 · HTML: https://mlsanity.com/paper/2209.07611/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Tailor: Generating and Perturbing Text with Semantic Controls](https://mlsanity.com/paper/2107.07150/) (2021)
- [Arabic Dialect Identification in the Wild](https://mlsanity.com/paper/2005.06557/) (2020)
- [How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs](https://mlsanity.com/paper/1612.04629/) (2016)
- [Computational Sociolinguistics: A Survey](https://mlsanity.com/paper/1508.07544/) (2015)
- [Learning to Recognize Dialect Features](https://mlsanity.com/paper/2010.12707/) (2020)
- [Snippext: Semi-supervised Opinion Mining with Augmented Data](https://mlsanity.com/paper/2002.03049/) (2020)
- [Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models](https://mlsanity.com/paper/2104.07578/) (2021)
- [Disambiguation of morpho-syntactic features of African American English -- the case of habitual be](https://mlsanity.com/paper/2204.12421/) (2022)

## Built on by (most cited first, 6 of 6)

- [Evaluation of African American Language Bias in Natural Language Generation](https://mlsanity.com/paper/2305.14291/) (2023)
- [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)
- [Data Caricatures: On the Representation of African American Language in Pretraining Corpora](https://mlsanity.com/paper/2503.10789/) (2025)
- [Analysis of LLM as a grammatical feature tagger for African American English](https://mlsanity.com/paper/2502.06004/) (2025)

## Abstract (arXiv)

The study of language variation examines how language varies between and within different groups of speakers, shedding light on how we use language to construct identities and how social contexts affect language use. A common method is to identify instances of a certain linguistic feature - say, the zero copula construction - in a corpus, and analyze the feature's distribution across speakers, topics, and other variables, to either gain a qualitative understanding of the feature's function or systematically measure variation. In this paper, we explore the challenging task of automatic morphosyntactic feature detection in low-resource English varieties. We present a human-in-the-loop approach to generate and filter effective contrast sets via corpus-guided edits. We show that our approach improves feature detection for both Indian English and African American English, demonstrate how it can assist linguistic research, and release our fine-tuned models for use by other researchers.