Learning to Recognize Dialect Features

By Dorottya Demszky, D. Sharma, J. Clark, Vinodkumar Prabhakaran, Jacob Eisenstein. 2020

43 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Language and Linguistics, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sebastian Gehrmann
2. Elizabeth Clark
3. Thibault Sellam
4. Sunipa Dev
5. Vinodkumar Prabhakaran
6. Aditya Siddhant
7. Shachi Dave
8. Partha P. Talukdar

In Language and Linguistics

1. Jacob Eisenstein
2. Partha P. Talukdar
3. Sophia Chan
4. Alona Fyshe

In Natural Language Processing

1. Thibault Sellam
2. Elizabeth Clark
3. Sebastian Gehrmann
4. Aditya Siddhant
5. Sunipa Dev
6. Jacob Eisenstein
7. Tu Vu
8. Timothy Dozat

Citations in the corpus

• Re-contextualizing Fairness in NLP: The Case of India (2022, methodology): “For this study, we use the minimal pairs dataset built by (Demszky et al., 2021) with 266 sentences annotated with presence of 22 morpho-syntactic dialectal features prevalent in Indian English.”
• Dialect-robust Evaluation of Generated Text (2022, background): “A related line of work has focused on inducing dialect features from corpora (Eisenstein et al., 2010; Jørgensen et al., 2015; Dunn, 2021) and on recognizing dialect features in text (Demszky et al., 2021; Masis et al., 2022).”