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A Sequence to Sequence Learning for Chinese Grammatical Error Correction

  • Hongkai Ren
  • Liner YangEmail author
  • Endong Xun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)

Abstract

Grammatical Error Correction (GEC) is an important task in natural language processing. In this paper, we introduce our system on NLPCC 2018 Shared Task 2 Grammatical Error Correction. The task is to detect and correct grammatical errors that occurred in Chinese essays written by non-native speakers of Mandarin Chinese. Our system is mainly based on the convolutional sequence-to-sequence model. We regard GEC as a translation task from the language of “bad” Chinese to the language of “good” Chinese. We describe the building process of the model in details. On the test data of NLPCC 2018 Shared Task 2, our system achieves the best precision score, and the \(F_{0.5}\) score is 29.02. Our final results ranked third among the participants.

Keywords

Grammatical Error Correction Convolutional Sequence to Sequence Model Neural machine translation 

Notes

Acknowledgment

This research project is supported by the funds of Beijing Advanced Innovation Center for Language Resources (No. TYR17001), the Key Project of National Social Science Foundation of China (No. 16AYY007), and the Fundamental Research Funds for the Central Universities in BLCU (No. 18YCX001). We also thank Huimeng Zhang, JingJing Miao and Jin Zhao for helping to modify the paper.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Beijing Advanced Innovation Center for Language ResourcesBeijingChina
  2. 2.School of Information ScienceBeijing Language and Culture UniversityBeijingChina

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