A Hybrid System for NLPTEA -2020 CGED Shared Task (2020)

Abstract: This paper introduces our system at NLPTEA2020 shared task for CGED, which is able to detect, locate, identify and correct grammatical errors in Chinese writings. The system consists of three components: GED, GEC, and post processing. GED is an ensemble of multiple BERT-based sequence labeling models for handling GED tasks. GEC performs error correction. We exploit a collection of heterogenous models, including Seq2Seq, GECToR and a candidate generation module to obtain correction candidates. Finally in the post processing stage, results from GED and GEC are fused to form the final outputs. We tune our models to lean towards optimizing precision, which we believe is more crucial in practice. As a result, among the six tracks in the shared task, our system performs well in the correction tracks: measured in F1 score, we rank first, with the highest precision, in the TOP3 correction track and third in the TOP1 correction track, also with the highest precision. Ours are among the top 4 to 6 in other tracks, except for FPR where we rank 12. And our system achieves the highest precisions among the top 10 submissions at IDENTIFICATION and POSITION tracks.

## introduction with the rapid growth of online education platforms and the advance of natural language processing (nlp) techniques, recent years have seen an increased interest in automatic grammatical error diagnosis (ged) and grammatical error correction (gec). shared tasks such as @xcite @xcite @xcite were held to correct grammatical errors in essays written by learners of english as a foreign language (efl). state-of-theart gec systems for efl learners have achieved impressive f 0.5 scores of 66.5 on conll-2014 (test) and 73.7 on @xcite . despite the great success of english gec systems, chinese grammatical error detection (cged) and correction (cgec) applications yet remain relatively unexplored. chinese, on the other hand, is quite different from western languages such as english: there are more than 3,000 commonly used chinese characters, while english has only 26 in total; chinese uses tones to indicate various meanings, while english uses them to express emotions; chinese emphasizes the meaning of expressions, usually resulting in short sentences without complex structure often seen in english. due to the large number of complex characters and flexible sentence structures, chinese is considered one of the most difficult languages in the world to learn. under this circumstance, the workshop on natural language processing techniques for educational applications (nlp-tea) has been organizing shared tasks for cged @xcite @xcite @xcite to help learners of chinese as a foreign language (cfl) since 2014. the shared tasks provide common test conditions for researchers from both industry and academia. we believe they are very beneficial to advancing cged technology. this paper introduces our work on this year's cged shared task. the task requires both error detection and correction, and we use a hybrid system to handle both. it uses as building blocks models designed for various nlp tasks, including bertbased sequence labeling models, seq2seq, and gector. we tune our models to lean towards optimizing precision, which we believe is more crucial in practice. the performance is further improved by using synthetic data generated for individual tasks. our system performs well in the correction tracks: measured in f1 score, we rank first, with the highest precision, in the top3 correction track and third in the top1 correction track, also with the highest precision. ours are among the top 4 to 6 in other tracks, except for fpr where we rank 12. and our system achieves the highest precisions among the top 10 submissions at identifica-tion and position tracks. the rest of this paper is organized as follows: a brief description of the cged shared task is given in section 2, followed by an overview of prior work in section 3. section 4 introduces our system in detail, and section 5 demonstrates the experimental results. finally, section 6 concludes this paper. ## task description generally, the cged shared task classifies grammatical errors found in chinese writings into four different classes, i.e., redundant words (r), missing words (m), word selection errors (s), word ordering errors (w). system performance is measured at the following levels: ## methodology the overall architecture of the developed system is depicted in fig. 1 . the proposed system can be functionally divided into three parts: ged, gec, and post-processing. the ged framework is responsible for error diagnosis at detection, identification and position levels, while the gec framework provides possible candidates for detected s and m errors. finally, the post-processing module takes results from the ged and gec frameworks and fuse them into the final form of the system outputs. ## conclusion this paper describes our system on nlptea-2020 cged shared task. to make the system more robust against data sparseness and lack of data, we adopt the synthetic data generation process during model training. besides utilizing up-to-date model architectures, we also carefully optimized the system performance by employing ensemble techniques, voting mechanisms and rule-based postprocessing. we plan to integrate more grammatical features into the ged and gec models and optimize the post-processing algorithm to further improve the system performance.