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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chollampatt, S., Ng, H.T.: Connecting the dots: towards human-level grammatical error correction. In: BEA@EMNLP (2017)
Chollampatt, S., Ng, H.T.: A multilayer convolutional encoder-decoder neural network for grammatical error correction. CoRR abs/1801.08831 (2018)
Chorowski, J., Bahdanau, D., Cho, K., Bengio, Y.: End-to-end continuous speech recognition using attention-based recurrent NN: first results. CoRR abs/1412.1602 (2014)
Dahlmeier, D., Ng, H.T.: Better evaluation for grammatical error correction. In: NAACL (2012)
Dauphin, Y., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: ICML (2017)
Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.: Convolutional sequence to sequence learning. In: ICML (2017)
Ji, J., Wang, Q., Toutanova, K., Gong, Y., Truong, S., Gao, J.: A nested attention neural hybrid model for grammatical error correction. In: ACL (2017)
Junczys-Dowmunt, M., Grundkiewicz, R.: Phrase-based machine translation is state-of-the-art for automatic grammatical error correction. In: EMNLP (2016)
Junczys-Dowmunt, M., Grundkiewicz, R., Guha, S., Heafield, K.: Approaching neural grammatical error correction as a low-resource machine translation task. CoRR abs/1804.05940 (2018)
Yu, L.-C., Lee, L.H., Chang, L.P.: Overview of grammatical error diagnosis for learning Chinese as foreign language. In: NLP-TEA (2014)
Ling, W., Dyer, C., Black, A.W., Trancoso, I.: Two/too simple adaptations of word2vec for syntax problems. In: NAACL (2015)
Neubig, G.: Neural machine translation and sequence-to-sequence models: a tutorial. CoRR abs/1703.01619 (2017)
Ng, H.T., Wu, S.M., Wu, Y., Hadiwinoto, C., Tetreault, J.R.: The CoNLL-2013 shared task on grammatical error correction. In: CoNLL Shared Task (2013)
Rao, G., Zhang, B., Xun, E., Lee, L.H.: IJCNLP-2017 task 1: Chinese grammatical error diagnosis. In: IJCNLP (2017)
Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. In: EMNLP (2015)
Schmaltz, A., Kim, Y., Rush, A.M., Shieber, S.M.: Adapting sequence models for sentence correction. In: EMNLP (2017)
Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. CoRR abs/1508.07909 (2016)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR 15, 1929–1958 (2014)
Yuan, Z., Briscoe, T.: Grammatical error correction using neural machine translation. In: NAACL (2016)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Ren, H., Yang, L., Xun, E. (2018). A Sequence to Sequence Learning for Chinese Grammatical Error Correction. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-99501-4_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99500-7
Online ISBN: 978-3-319-99501-4
eBook Packages: Computer ScienceComputer Science (R0)