C hinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation (2020)

Abstract: A better Chinese Grammatical Error Diagnosis (CGED) system for automatic Grammatical Error Correction (GEC) can benefit foreign Chinese learners and lower Chinese learning barriers. In this paper, we introduce our solution to the CGED2020 Shared Task Grammatical Error Correction in detail. The task aims to detect and correct grammatical errors that occur in essays written by foreign Chinese learners. Our solution combined data augmentation methods, spelling check methods, and generative grammatical correction methods, and achieved the best recall score in the Top 1 Correction track. Our final result ranked fourth among the participants.

## introduction in recent years, a global upsurge of chinese learning has been set off. however, due to the language environment and language structure differences between countries, foreign chidue to the deficiency of parallel corpora, chinese gec often used statistical methods and rule-based methods in the early stage. until recently, with larger-scale parallel corpora developed, machine learning techniques were applied to the chinese gec task. chen @xcite used an approach based on the conditional random fields (crf) model. the model added a collocation feature in order to better identify grammatical errors in word choice. zhengn @xcite used a crf based model, along with an rnn based model and an ensemble model, reached high f1-scores and recall rates across the three assessment levels of the nlp-tea-3 shared task. yang @xcite leveraged a bi-lstm-crf model. spliced word vectors with manual features such as bi-gram, pos, and pmi were added during training. fu @xcite also used a bi-lstm-crf model, with epmi values integrated. they obtained promising results in the cged2018 shared task. in this paper, we introduce our solution to the cged2020 shared tasks. by combining data augmentation methods, spelling check methods, and generative grammatical correction methods, we achieved the best recall score in the top 1 correction track. our final result ranked fourth among the participants. the rest of this article is organized as follows: section 2 describes the shared tasks of cged2020. section 3 describes the methods used in this paper, including data preprocessing, data augmentation, and various deep learning error correction models. section 4 conducts experiments on the methods mentioned above. in section 5, the conclusion and the planning for future works are given. ## task definition the cged2020 shared task is the sixth chinese grammar diagnosis error competition, held since 2014. this task provides participants a shared data set using the writing part of hanyu shuiping kaoshi (hsk). the goal and direction of the task are to use modern nlp techniques to detect foreign chinese learners' grammatical errors in chinese writing and build an automatic chinese grammatical error diagnosis system. it mainly distinguishes four different types of errors, including redundant words (r), missing words(m), word selection (s), and word order error(w). on this basis, a comprehensive evaluation is carried out according to these dimensions of error judgment of the problem sentence, error type analysis, the error location, and sentence modification suggestions. the detailed sample is shown in the criteria for judging correctness are determined at three levels as follows. ## experiments we first train lasertagger on hsk+lang8 dataset to obtain laser_raw.