Novel Benchmark Data Set for Automatic Error Detection and Correction

1. Rei, M., Felice, M., Yuan, Z., Briscoe, T.: Artificial error generation with machine translation and syntactic patterns. CoRR, abs/1707.05236 (2017)

   Google Scholar 

2. Bryant, C., Yuan, Z., Qorib, M.R., Cao, H., Ng, H.T., Briscoe, T.: Grammatical error correction: a survey of the state of the art (2022)

   Google Scholar 

3. Hládek, D., Staš, J., Pleva, M.: Survey of automatic spelling correction. Electronics 9(10), 1670 (2020)

   Article  Google Scholar 

4. Pirinen, T.A., Lindén, K.: State-of-the-art in weighted finite-state spell-checking. In: Gelbukh, A. (ed.) CICLing 2014, Part II. LNCS, vol. 8404, pp. 519–532. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54903-8_43

   Chapter  Google Scholar 

5. Ali, M., Khalid, S., Rana, M.I., Azhar, F.: A probabilistic framework for short text classification. In: IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018, Las Vegas, NV, USA, 8–10 January 2018, pp. 742–747. IEEE (2018)

   Google Scholar 

6. Wilcox-O’Hearn, A., Hirst, G., Budanitsky, A.: Real-word spelling correction with trigrams: a reconsideration of the mays, Damerau, and Mercer model. In: Gelbukh, A. (ed.) CICLing 2008. LNCS, vol. 4919, pp. 605–616. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78135-6_52

   Chapter  Google Scholar 

7. Fossati, D., Di Eugenio, B.: A mixed trigrams approach for context sensitive spell checking. In: Gelbukh, A. (ed.) CICLing 2007. LNCS, vol. 4394, pp. 623–633. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70939-8_55

   Chapter  Google Scholar 

8. Islam, A., Inkpen, D.: Real-word spelling correction using google web 1t 3-grams. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, 6–7 August 2009, Singapore, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1241–1249. ACL (2009)

   Google Scholar 

9. Rozovskaya, A., Roth, D.: Grammatical error correction: machine translation and classifiers. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 August 2016, Berlin, Germany, Volume 1: Long Papers. The Association for Computer Linguistics (2016)

   Google Scholar 

10. Yannakoudakis, H., Rei, M., Andersen, Ø.E., Yuan, Z.: Neural sequence-labelling models for grammatical error correction. In: Palmer, M., Hwa, R., Riedel, S. (eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017, pp. 2795–2806. Association for Computational Linguistics (2017)

    Google Scholar 

11. Yuan, Z., Briscoe, T.: Grammatical error correction using neural machine translation. In: Knight, K., Nenkova, A., Rambow, O. (eds.) 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. The Association for Computational Linguistics (2016)

    Google Scholar 

12. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) 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. 1724–1734. ACL (2014)

    Google Scholar 

13. Chollampatt, S., Ng, H.T.: A multilayer convolutional encoder-decoder neural network for grammatical error correction. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, 2–7 February 2018, pp. 5755–5762. AAAI Press (2018)

    Google Scholar 

14. Zhang, S., Huang, H., Liu, J., Li, H.: Spelling error correction with soft-masked BERT. CoRR, abs/2005.07421 (2020)

    Google Scholar 

15. Moslem, Y., Haque, R., Way, A.: Adaptive machine translation with large language models. CoRR, abs/2301.13294 (2023)

    Google Scholar 

16. Bryant, C., Briscoe, T.: Language model based grammatical error correction without annotated training data. In: Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 247–253, New Orleans, Louisiana. Association for Computational Linguistics (2018)

    Google Scholar 

17. Alikaniotis, D., Raheja, V.: The unreasonable effectiveness of transformer language models in grammatical error correction. CoRR, abs/1906.01733 (2019)

    Google Scholar 

18. Tan, M., Chen, D., Li, Z., Wang, P.: Spelling error correction with BERT based on character-phonetic. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 1146–1150. IEEE (2020)

    Google Scholar 

19. Bryant, C., Yuan, Z., Qorib, M.R., Cao, H., Ng, H.T., Briscoe, T.: Grammatical error correction: a survey of the state of the art. CoRR, abs/2211.05166 (2022)

    Google Scholar 

20. Dahlmeier, D., Ng, H.T., Wu, S.M.: Building a large annotated corpus of learner English: the NUS corpus of learner English. In: Tetreault, J.R., Burstein, J., Leacock, C. (eds.) 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. The Association for Computer Linguistics (2013)

    Google Scholar 

21. Bryant, C., Felice, M., Andersen, Ø.E., Briscoe, T.: The BEA-2019 shared task on grammatical error correction. In: Yannakoudakis, H., Kochmar, E., Leacock, C., Madnani, N., Pilán, I., Zesch, T. (eds.) Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, BEA@ACL 2019, Florence, Italy, 2 August 2019, pp. 52–75. Association for Computational Linguistics (2019)

    Google Scholar 

22. Etoori, P., Chinnakotla, M., Mamidi, R.: Automatic spelling correction for resource-scarce languages using deep learning. In: Shwartz, V., Tabassum, J., Voigt, R., Che, W., de Marneffe, M.-C., Nissim, M. (eds.) Proceedings of ACL 2018, Melbourne, Australia, 15–20 July 2018, Student Research Workshop, pp. 146–152. Association for Computational Linguistics (2018)

    Google Scholar 

23. Näther, M.: An in-depth comparison of 14 spelling correction tools on a common benchmark. In: Calzolari, N., et al. (eds.) Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, 11–16 May 2020, pp. 1849–1857. European Language Resources Association (2020)

    Google Scholar 

24. Xue, L., et al.: mT5: a massively multilingual pre-trained text-to-text transformer. In: Toutanova, K., et al. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, 6–11 June 2021, pp. 483–498. Association for Computational Linguistics (2021)

    Google Scholar 

25. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 140:1–140:67 (2020)

    Google Scholar 

26. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805 (2018)

    Google Scholar 

Download references