  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.
