System Report for CCL 23-Eval Task 8: C hinese Grammar Error Detection and Correction Using Multi-Granularity Information (2023)

Abstract: “This paper introduces our system at CCL-2023 Task: Chinese Essay Fluency Evaluation (CEFE).The CEFE task aims to study the identification and correction of grammatical errors in primaryand middle school students’ test compositions. The evaluation has three tracks to examine therecognition of wrong sentence types, character-level error correction, and wrong sentence rewrit-ing. According to the task characteristics and data distribution of each track, we propose a token-level discriminative model based on sequence labeling for the multi-label classification task ofwrong sentences, an auto-encoder model based on edited labels for character-level error correc-tion and a seq2seq model obtained by pre-training on pseudo data and fine-tuning on labeleddata to solve the wrong sentence rewriting task. In the final evaluation results, the method weproposed won the first place in all three tracks according to the corresponding evaluation metrics.”

## introduction with the development of the internet, the scale of online texts is also increasing, and it is difficult to meet the needs of text proofreading by relying on manual review alone. especially in some error-intensive fields, such as the composition evaluation of elementary and middle school students, manual evaluation has become expensive and inefficient. at this time, it becomes very necessary to use deep learning technology to build an efficient evaluation system, which can assist teachers in identifying. in order to promote the development of the field of text error correction, the china national conference on computational linguistics (ccl-2023) has taken chinese essay fluency evaluation (cefe) as one of the shared tasks. this task systematically classifies text errors at different granularities, provides human-annotated data, and proposes three tracks covering error correction and error detection. in this work, we introduce our method at ccl-2023 cefe task. for error detection, we adopt a finegrained error detection model based on sequence annotation. sentence-level multi-label tasks are accomplished by discriminating the type of error involved in each token. at the same time, we use techniques such as model inheritance and threshold post-processing to alleviate the bias caused by pseudo-data training. due to the misalignment between the sequence labeling task and the provided human-annotated data, we constructed a large amount of pseudo-data for various types of errors based on ltp @xcite ) and heuristic rules, which were used for the training of the track1 model and the pre-training of track2 and track3 models. for error correction, we trained an auto-encoder model based on edit label prediction and an auto-regressive model of seq2seq for character-level errors and extensive errors (including character-level and component-level), respectively. in the final evaluation, our method won the first place in the three tracks of wrong sentence type discrimination, character-level error correction, and wrong sentence rewriting. this article is organized as follows: section 2 briefly introduces the cefe shared task; section 3 mainly expounds the methods we use in this evaluation, including data level and model level; section 4 presents the main experimental results; section 5 introduces some related work on text error correction; finally, we conclude in section 6 with reflections on future work. ## chinese essay fluency evaluation the goal of the ccl2023 cefe task is to identify wrong sentences in primary and middle school students' compositions, judge the wrong sentence category they belong to, and propose amendments. the previous work did not carry out a detailed classification of wrong sentences. this evaluation is the first to start from the two perspectives of character-level errors and component-level errors, and defines four categories of coarse-grained error categories (character-level error, component incompleteness error, component redundancy error, and component mismatch error) , which contain 14 fine-grained error categories, as shown in specifically, track 1 of the task is mainly dedicated to identifying the error types of wrong sentences, track 2 requires the identification and correction of character-level coarse-grained errors in sentences, and track 3 requires the rewriting of wrong sentences containing extensive errors. ## methodology according to the requirements of each track, the system we submitted needs to complete the identification and error correction of wrong sentences. below we will introduce our method from these two aspects. ## conclusion and future work this paper describes our detection and correction system on the ccl-2023 cefe task, which includes token-level error correction and the implementation of two different paradigm gec models. in all three tracks, the metrics of our model reached the first. in future work, we will continue to study more efficient grammatical error correction methods, such as the adaptation of the seq2seq model to error correction tasks, the improvement of the speed of autoregressive methods, and the utilization of llms like chatgpt, etc.