  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.
