We present a novel beam-search decoder for grammatical error correction. The decoder iteratively generates new hypothesis corrections from current hypotheses and scores them based on features of grammatical correctness and fluency. These features include scores from discriminative classifiers for specific error categories, such as articles and prepositions. Unlike all previous approaches, our method is able to perform correction of whole sentences with multiple and interacting errors while still taking advantage of powerful existing classifier approaches. Our decoder achieves an F 1 correction score significantly higher than all previous published scores on the Helping Our Own (HOO) shared task data set.
Recently, REF presented a beam-search decoder for correcting spelling, article, preposition, punctuation and noun number errors.
A Beam-Search Decoder for Grammatical Error Correction
EMNLP
Computer Science