Neural Sequence-Labelling Models for Grammatical Error Correction
Helen Yannakoudakis, Marek Rei, Øistein E. Andersen, Zheng Yuan · 2017
We propose an approach to N -best list reranking using neural sequence-labelling models.We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context.Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems.Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.