System Report for CCL 23-Eval Task 7: C hinese Grammatical Error Diagnosis Based on Model Fusion (2023)

Abstract: “The purpose of the Chinese Grammatical Error Diagnosis task is to identify the positions andtypes of grammar errors in Chinese texts. In Track 2 of CCL2023-CLTC, Chinese grammarerrors are classified into four categories: Redundant Words, Missing Words, Word Selection, andWord Ordering Errors. We conducted data filtering, model research, and model fine-tuning insequence. Then, we performed weighted fusion of models based on perplexity calculations andintroduced various post-processing strategies. As a result, the performance of the model on thetest set, measured by COM, reached 49.12.”

## introduction the purpose of the chinese grammatical error diagnosis (cged) task is to detect the location and type of each grammatical error in the chinese text. the types of grammatical errors are divided into four categories: redundant words (r), missing words (m), word selection (s), and word ordering errors (w). in recent years, the task of chinese grammar error correction has attracted more and more attention, and some applications with potential commercial value have also appeared. this technology has a broad application space in education, news, official documents and other fields. the mainstream methods to solve this task are seq2seq and seq2edits. the seq2seq method regards the grammatical error correction task as the process of translating an erroneous sentence into a correct sentence, and uses an advanced neural translation model to solve it; the seq2edits method is to design editing actions (such as insertion, deletion, replacement, etc.), the grammar diagnosis task is regarded as a sequence labeling task to solve. in the ccl2023-cltc track 2: chinese grammar error detection task, we use the multi-model fusion method and post-processing strategy to realize the text grammar error correction function. finally, on the ccl2023-cltc track 2 chinese grammar error diagnosis task, the result of com is 49.12. ## model we did a lot of research on models and papers when we were doing the task of chinese grammar error detection in track 2. the mainstream methods to solve this task are seq2seq and seq2edits. the benchmark models we choose are the current mainstream bart (bidirectional and auto-regressive transformers) @xcite , gector (grammatical error correction: tag, not rewrite) and t5 (text-to-text transfer transformer) that have achieved sota performance on the cgec (chinese grammatical error correction) dataset. the following is a detailed introduction to the models we use in this task. ## data track 2 provides the processed lang8 dataset and cged dataset @xcite . the word count statistics of the lang8 dataset and the official test set are shown in