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        <title>Connecting the Dots: Towards Human-Level Grammatical Error Correction</title>
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        <namePart type="given">Shamil</namePart>
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            <title>Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications</title>
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            <namePart type="given">Joel</namePart>
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    <abstract>We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address this issue, we incorporate a character-level SMT component targeting the misspelled words that the original SMT-based system fails to correct. Our final system achieves 53.14% F 0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62% F 0.5 over the best previous published score.</abstract>
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%0 Conference Proceedings
%T Connecting the Dots: Towards Human-Level Grammatical Error Correction
%A Chollampatt, Shamil
%A Ng, Hwee Tou
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F chollampatt-ng-2017-connecting
%X We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address this issue, we incorporate a character-level SMT component targeting the misspelled words that the original SMT-based system fails to correct. Our final system achieves 53.14% F 0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62% F 0.5 over the best previous published score.
%R 10.18653/v1/W17-5037
%U https://aclanthology.org/W17-5037
%U https://doi.org/10.18653/v1/W17-5037
%P 327-333

Markdown (Informal)

[Connecting the Dots: Towards Human-Level Grammatical Error Correction](https://aclanthology.org/W17-5037) (Chollampatt & Ng, BEA 2017)

• Connecting the Dots: Towards Human-Level Grammatical Error Correction (Chollampatt & Ng, BEA 2017)

ACL

• Shamil Chollampatt and Hwee Tou Ng. 2017. Connecting the Dots: Towards Human-Level Grammatical Error Correction. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 327–333, Copenhagen, Denmark. Association for Computational Linguistics.