What can NLP do for linguistics? Towards using grammatical error analysis to document non-standard English features

Abstract This paper proposes a novel use of grammatical error detection/correction (GED/GEC) tools to document non-standard English varieties. This is motivated by the fact that both GED/GEC technology and sociolinguistics aim to identify linguistic deviation from the so-called standard, yet there has been little communication between the two fields. We thus investigate whether state-of-the-art GED/GEC models can be effectively repurposed to automatically detect dialectal differences and assist linguistic missions in this regard. We explore this in the context of written Singaporean English (Cambridge Write & Improve), and spoken Vietnamese English (CanVEC), representing an established non-standard variety (Case 1) and an emerging variety (Case 2), respectively. We find that our GED/GEC systems are able to successfully detect a number of both established and new features. We further highlight some of the remaining areas that the systems overlook, as well as opportunities for future developments. This research bridges the gap between GED/GEC and dialectology, emphasizing their shared theme of linguistic deviation from a socially defined standard.