Paper: Predicting Grammaticality on an Ordinal Scale

ACL ID P14-2029
Title Predicting Grammaticality on an Ordinal Scale
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

Automated methods for identifying whether sentences are grammatical have various potential applications (e.g., machine translation, automated essay scoring, computer-assisted language learning). In this work, we construct a statistical model of grammaticality using various linguistic features (e.g., mis- spelling counts, parser outputs, n-gram language model scores). We also present a new publicly available dataset of learner sentences judged for grammaticality on an ordinal scale. In evaluations, we compare our system to the one from Post (2011) and find that our approach yields state-of-the-art performance.