Before revision:  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 .
After revision:  Linguists typically define dialect features by providing a small number of minimal pairs, building on how linguists typically define dialect features. Evaluation on a test set of 22 dialect features .