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        <title>Neural Sequence-Labelling Models for Grammatical Error Correction</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Neural Sequence-Labelling Models for Grammatical Error Correction
%A Yannakoudakis, Helen
%A Rei, Marek
%A Andersen, Øistein E.
%A Yuan, Zheng
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yannakoudakis-etal-2017-neural
%X 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.
%R 10.18653/v1/D17-1297
%U https://aclanthology.org/D17-1297/
%U https://doi.org/10.18653/v1/D17-1297
%P 2795-2806

Markdown (Informal)

[Neural Sequence-Labelling Models for Grammatical Error Correction](https://aclanthology.org/D17-1297/) (Yannakoudakis et al., EMNLP 2017)

• Neural Sequence-Labelling Models for Grammatical Error Correction (Yannakoudakis et al., EMNLP 2017)

ACL

• Helen Yannakoudakis, Marek Rei, Øistein E. Andersen, and Zheng Yuan. 2017. Neural Sequence-Labelling Models for Grammatical Error Correction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2795–2806, Copenhagen, Denmark. Association for Computational Linguistics.