Advertisement

Automatic Grammatical Error Correction Based on Edit Operations Information

  • Quanbin Wang
  • Ying Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)

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.

Keywords

Grammatical error correction Edit operations Natural language processing Semantically conditioned LSTM Sequence to sequence 

Notes

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.

References

  1. 1.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  2. 2.
    Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Advances in Neural Information Processing Systems 28, Annual Conference on Neural Information Processing Systems 2015, 7–12 December 2015, Montreal, Quebec, Canada, pp. 1171–1179 (2015)Google Scholar
  3. 3.
    Brockett, C., Dolan, W.B., Gamon, M.: Correcting ESL errors using phrasal SMT techniques. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia. Association for Computational Linguistics, pp. 249–256 (2006)Google Scholar
  4. 4.
    Bryant, C., Felice, M., Briscoe, T.: Automatic annotation and evaluation of error types for grammatical error correction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July–4 August, Volume 1: Long Papers, pp. 793–805 (2017).  https://doi.org/10.18653/v1/P17-1074
  5. 5.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  6. 6.
    Chollampatt, S., Ng, H.T.: Connecting the dots: towards human-level grammatical error correction. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, BEA@EMNLP 2017, Copenhagen, Denmark, 8 September 2017, pp. 327–333 (2017). https://aclanthology.info/papers/W17-5037/w17-5037
  7. 7.
    Chollampatt, S., Ng, H.T.: A multilayer convolutional encoder-decoder neural network for grammatical error correction. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, 2–7 February 2018 (2018). https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17308
  8. 8.
    Chollampatt, S., Taghipour, K., Ng, H.T.: Neural network translation models for grammatical error correction. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp. 2768–2774 (2016). http://www.ijcai.org/Abstract/16/393
  9. 9.
    Dahlmeier, D., Ng, H.T.: Better evaluation for grammatical error correction. In: Human Language Technologies, Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, 3–8 June 2012, Montréal, Canada, pp. 568–572 (2012). http://www.aclweb.org/anthology/N12-1067
  10. 10.
    Dahlmeier, D., Ng, H.T., Wu, S.M.: Building a large annotated corpus of learner English: the NUS corpus of learner English. In: Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications, BEA@NAACL-HLT 2013, 13 June 2013, Atlanta, Georgia, USA, pp. 22–31 (2013). http://aclweb.org/anthology/W/W13/W13-1703.pdf
  11. 11.
    Felice, M., Bryant, C., Briscoe, T.: Automatic extraction of learner errors in ESL sentences using linguistically enhanced alignments. In: COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 11–16 December 2016, Osaka, Japan, pp. 825–835 (2016). http://aclweb.org/anthology/C/C16/C16-1079.pdf
  12. 12.
    Felice, M., Yuan, Z., Andersen, Ø.E., Yannakoudakis, H., Kochmar, E.: Grammatical error correction using hybrid systems and type filtering. In: Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, CoNLL 2014, Baltimore, Maryland, USA, 26–27 June 2014, pp. 15–24 (2014). http://aclweb.org/anthology/W/W14/W14-1702.pdf
  13. 13.
    Francis, W.N., Kucera, H.: The brown corpus: a standard corpus of present-day edited American English. Department of Linguistics, Brown University [producer and distributor], Providence, RI (1979)Google Scholar
  14. 14.
    Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, pp. 1243–1252 (2017). http://proceedings.mlr.press/v70/gehring17a.html
  15. 15.
    Grundkiewicz, R., Junczys-Dowmunt, M.: Near human-level performance in grammatical error correction with hybrid machine translation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, 1–6 June 2018, Volume 2 (Short Papers), pp. 284–290 (2018). https://aclanthology.info/papers/N18-2046/n18-2046
  16. 16.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385
  17. 17.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  18. 18.
    Ji, J., Wang, Q., Toutanova, K., Gong, Y., Truong, S., Gao, J.: A nested attention neural hybrid model for grammatical error correction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July 30–4 August, Volume 1: Long Papers, pp. 753–762 (2017).  https://doi.org/10.18653/v1/P17-1070
  19. 19.
    Junczys-Dowmunt, M., Grundkiewicz, R.: The AMU system in the CoNLL-2014 shared task: grammatical error correction by data-intensive and feature-rich statistical machine translation. In: Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, CoNLL 2014, Baltimore, Maryland, USA, 26–27 June 2014, pp. 25–33 (2014). http://aclweb.org/anthology/W/W14/W14-1703.pdf
  20. 20.
    Junczys-Dowmunt, M., Grundkiewicz, R.: Phrase-based machine translation is state-of-the-art for automatic grammatical error correction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, 1–4 November 2016, pp. 1546–1556 (2016). http://aclweb.org/anthology/D/D16/D16-1161.pdf
  21. 21.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
  22. 22.
    Macdonald, N., Frase, L., Gingrich, P., Keenan, S.: The writer’s workbench: computer aids for text analysis. IEEE Trans. Commun. 30(1), 105–110 (1982)CrossRefGoogle Scholar
  23. 23.
    Ng, H.T., Wu, S.M., Briscoe, T., Hadiwinoto, C., Susanto, R.H., Bryant, C.: The CoNLL-2014 shared task on grammatical error correction. In: Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, CoNLL 2014, Baltimore, Maryland, USA, 26–27 June 2014, pp. 1–14 (2014). http://aclweb.org/anthology/W/W14/W14-1701.pdf
  24. 24.
    Ng, H.T., Wu, S.M., Wu, Y., Hadiwinoto, C., Tetreault, J.R.: The CoNLL-2013 shared task on grammatical error correction. In: Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task, CoNLL 2013, Sofia, Bulgaria, 8–9 August 2013, pp. 1–12 (2013). http://aclweb.org/anthology/W/W13/W13-3601.pdf
  25. 25.
    Schmaltz, A., Kim, Y., Rush, A.M., Shieber, S.M.: Adapting sequence models for sentence correction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017, pp. 2807–2813 (2017). https://aclanthology.info/papers/D17-1298/d17-1298
  26. 26.
    Susanto, R.H., Phandi, P., Ng, H.T.: System combination for grammatical error correction. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar. A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 951–962 (2014). http://aclweb.org/anthology/D/D14/D14-1102.pdf
  27. 27.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)Google Scholar
  28. 28.
    Tajiri, T., Komachi, M., Matsumoto, Y.: Tense and aspect error correction for ESL learners using global context. In: The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 8–14 July 2012, Jeju Island, Korea - Volume 2: Short Papers, pp. 198–202 (2012). http://www.aclweb.org/anthology/P12-2039
  29. 29.
    Zhang, K.L., Wang, H.F.: A unified framework for grammar error correction. In: CoNLL-2014, pp. 96–102 (2014)Google Scholar
  30. 30.
    Wen, T., Gasic, M., Mrksic, N., Su, P., Vandyke, D., Young, S.J.: Semantically conditioned LSTM-based natural language generation for spoken dialogue systems. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17–21 September 2015, pp. 1711–1721 (2015). http://aclweb.org/anthology/D/D15/D15-1199.pdf
  31. 31.
    Xie, Z., Avati, A., Arivazhagan, N., Jurafsky, D., Ng, A.Y.: Neural language correction with character-based attention. arXiv preprint arXiv:1603.09727 (2016)
  32. 32.
    Yuan, Z., Briscoe, T.: Grammatical error correction using neural machine translation. In: NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, 12–17 June 2016, pp. 380–386 (2016). http://aclweb.org/anthology/N/N16/N16-1042.pdf

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Laboratory of Machine Perception (Ministry of Education) and Department of Machine Intelligence, School of Electronics Engineering and Computer SciencePeking UniversityBeijingPeople’s Republic of China

Personalised recommendations