Abstract：The Grammatical Error Correction (GEC) task is to realize automatic error detection and correction of text through natural language processing technology, such as word order, spelling and other grammatical errors. Many existing Chinese GEC methods have achieved good results, but these methods have not taken into characteristics of learners, such as level,native language and so on. Therefore, this paper proposes to personalize the GEC model to the characteristics of Chinese as a Second Language (CSL) learners and correct the mistakes made by CSL learners with different characteristics. To verify our method, we construct domain adaptation datasets. Experiment results on the domain adaptation datasets demonstrate that the performance of the GEC model is greatly improved after adapting to various domains of CSL learners.
［1］Lee J, Seneff S. An analysis of grammatical errors in non-native speech in English［C］//Proceedings of the IEEE Spoken Language Technology Workshop. IEEE, 2008: 89-92.
［2］Chollampatt S, Ng H T. A multilayer convolutional encoder-decoder neural network for grammatical error correction［C］//Proceedings of the AAAI Conference on Artificial Intelligence, 2018.
［3］Junczys Dowmunt M, Grundkiewicz R, Guha S, et al. Approaching neural grammatical error correction as a low-resource machine translation task［C］//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 595-606.
［4］Nadejde M, Tetreault J. Personalizing grammatical error correction: Adaptation to proficiency level and L1［C］//Proceedings of the 5th Workshop on Noisy User-generated Text, 2019: 27-33.
［5］Ren H, Yang L, Xun E. A sequence to sequence learning for Chinese grammatical error correction［C］//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, Cham, 2018: 401-410.
［6］Fu K, Huang J, Duan Y. Youdaos winning solution to the NLPCC-2018 task 2 challenge: A neural machine translation approach to Chinese grammatical error correction［C］//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, Cham, 2018: 341-350.
［7］Zhou J, Li C, Liu H, et al. Chinese grammatical error correction using statistical and neural models［C］//Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, Cham, 2018: 117-128.
［8］Zhao Z, Wang H. MaskGEC: Improving neural grammatical error correction via dynamic masking［C］//Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(01): 1226-1233.
［9］王辰成, 杨麟儿, 王莹莹, 等. 基于 Transformer 增强架构的中文语法纠错方法［J］. 中文信息学报, 2020, 34(6): 106-114.
［10］Madotto A, Lin Z, Wu C S, et al. Personalizing dialogue agents via meta-learning［C］//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 5454-5459.
［11］Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need［C］//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
［12］Sennrich R, Haddow B, Birch A. Neural machine translation of rare words with subword units［C］//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 1715-1725.
［13］Bustamante F R, León F S. GramCheck: A grammar and style checker［C］//Proceedings of the 16th Conference on Computational Linguistics, 1996: 175-181.
［14］Brockett C. Correcting ESL errors using phrasal SMT techniques［C］//Proceedings of the 21th International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, 2006: 249-256.
［15］Yuan Z, Briscoe T. Grammatical error correction using neural machine translation［C］//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016: 380-386.
［16］Ji J, Wang Q, Toutanova K, et al. A nested attention neural hybrid model for grammatical error correction［C］//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 753-762.
［17］周小兵, 朱其智, 邓小宁. 外国人学汉语语法偏误研究［M］. 北京：北京语言大学出版社, 2007.
［18］Smith B . Learner English: A teachers guide to interference and other problems［M］. Ernst Klett Sprachen, 2001.
［19］Rozovskaya A, Roth D. Algorithm selection and model adaptation for ESL correction tasks［C］//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011: 924-933.
［20］Mizumoto T, Komachi M, Nagata M, et al. Mining revision log of language learning SNS for automated Japanese error correction of second language learners［C］//Proceedings of the 5th International Joint Conference on Natural Language Processing, 2011: 147-155.
