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Automatic Grammatical Error Correction Based on Edit Operations Information

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

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Abstract

For second language learners, a reliable and effective Grammatical Error Correction (GEC) system is imperative, since it can be used as an auxiliary assistant for errors correction and helps learners improve their writing ability. Researchers have paid more emphasis on this task with deep learning methods. Better results were achieved on the standard benchmark datasets compared to traditional rule based approaches. We treat GEC as a special translation problem which translates wrong sentences into correct ones like other former works. In this paper, we propose a novel correction system based on sequence to sequence (Seq2Seq) architecture with residual connection and semantically conditioned LSTM (SC-LSTM), incorporating edit operations as special semantic information. Our model further improves the performance of neural machine translation model for GEC and achieves state-of-the-art \(F _{0.5}\)-score on standard test data named CoNLL-2014 compared with other methods that without any re-rank approach.

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Acknowledgments

This work was supported by the Natural Science Foundation of China (NSFC) under grant no. 61673025 and 61375119 and Supported by Beijing Natural Science Foundation (4162029), and partially supported by National Key Basic Research Development Plan (973 Plan) Project of China under grant no. 2015CB352302.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (Ministry of Education) and Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, People’s Republic of China

    Quanbin Wang & Ying Tan

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  1. Quanbin Wang
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  2. Ying Tan
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Correspondence to Ying Tan .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Wang, Q., Tan, Y. (2018). Automatic Grammatical Error Correction Based on Edit Operations Information. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_44

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  • DOI: https://doi.org/10.1007/978-3-030-04221-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

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