Data Driven Grammatical Error Detection in Transcripts of Children's Speech

We investigate grammatical error detection in spoken language, and present a data-driven method to train a dependency parser to automatically identify and label grammatical errors. This method is agnostic to the label set used, and the only manual annotations needed for training are grammatical error labels. We find that the proposed system is robust to disfluencies, so that a separate stage to elide disfluencies is not required. The proposed system outperforms two baseline systems on two different corpora that use different sets of error tags. It is able to identify utterances with grammatical errors with an F1-score as high as 0.623, as compared to a baseline F1 of 0.350 on the same data.