MD3: The Multi-Dialect Dataset of Dialogues

We introduce a new dataset of conversational speech representing English from India, Nigeria, and the United States. The Multi-Dialect Dataset of Dialogues (MD3) strikes a new balance between open-ended conversational speech and task-oriented dialogue by prompting participants to perform a series of short information-sharing tasks. This facilitates quantitative cross-dialectal comparison, while avoiding the imposition of a restrictive task structure that might inhibit the expression of dialect features. Preliminary analysis of the dataset reveals significant differences in syntax and in the use of discourse markers. The dataset, which will be made publicly available with the publication of this paper, includes more than 20 hours of audio and more than 200,000 orthographically-transcribed tokens.

Learning to Recognize Dialect Features

Linguists characterize dialects by the presence, absence, and frequency of dozens of interpretable features. Detecting these features in text has applications to social science and dialectology, and can be used to assess the robustness of natural language processing systems to dialect differences. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. Linguists typically define dialect features by providing a small number of minimal pairs, which are paired examples distinguished only by whether the feature is present, while holding everything else constant. In this paper, we present two multitask learning architectures for recognizing dialect features, both based on pretrained transformers. We evaluate these models on two test sets of Indian English, annotated for a total of 22 dialect features. We find these models learn to recognize many features with high accuracy; crucially, a few minimal pairs can be nearly as effective for training as thousands of labeled examples. We also demonstrate the downstream applicability of our dialect feature detection model as a dialect density measure and as a dialect classifier.