# A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging
## 1 Introduction  
In recent years, rapid development in China has encouraged many people to learn Chinese. The number of foreigners who regard Chinese as their second language is constantly increasing. Because the grammatical structure and usage habits of different languages are very different, learners of Chinese are prone to grammatical errors when writing Chinese. In addition, with the booming of the Internet, a huge amount of textual information has appeared in our lives in the form of news reports, blogs, emails and chat logs. Inevitably, there are many grammatically incorrect texts due to the lack of secondary review. Moreover, text generated by methods such as machine translation, speech recognition and image text recognition may contain errors. These Chinese grammatical errors not only seriously affect the user experience but also affect the computer's understanding of the semantics, causing the computer to make incorrect judgments. Therefore, the question of how to detect and correct grammatical errors in Chinese texts deserves further research. Grammatical error correction (GEC) refers to generating grammatically correct sentences from incorrect sentences, thereby enhancing the user experience and reducing the cost of manual verification. Although GEC can be used in many languages, in this paper, we focus only on Chinese GEC.  
There are two typical types of grammar error correction methods used in this academic field: sequence generation and sequence tagging. Because the sequence generation method is similar to neural machine translation (NMT), the authors in [1] used the NMT approach based on sequence-to-sequence (Seq2 Seq) models to address the GEC problem and made some progress. However, the sequence generation method requires sequential decoding, resulting in low efficiency. The sequence tagging approach was first applied to the GEC task in [2]. Sequence tagging methods predict the corresponding edit operation, while sequence generation methods predict the correct sentence directly. For this reason, the sequence tagging method allows us to better observe and control the correction process. Compared with the sequence generation method, the sequence tagging method not only has stronger interpretability and controllability but also has a faster processing speed.  
The sequence tagging model can only tag each token once, so only one edit operation can be performed on each token by the model. However, many errors cannot be corrected in a single edit. The current state-of-the-art method GECToR [3] uses multiple iterations to tag and correct sentences, correcting only the more significant errors in each round. However, each iteration relies on the error correction results of the previous round, which leads to exposure bias. To address the problem, inspired by Google's TeaForN [4], we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT) to extend the GECToR model, to improve the effectiveness of the Chinese GEC task.  
Our contributions are summarized as follows:  
1. We propose a Chinese GEC method based on iterative training, which aligns the model training process with the inference process to solve the problem of exposure bias caused by using iterative inference.
2. We design a sequence labeling method that combines direct prediction with prediction using conditional random fields (CRF) [5], allowing the model to focus more on the tagging results of the whole sequence.
3. We implement a focal loss [6] penalty strategy for the loss function to alleviate the class imbalance problem in the classification problem.
4. Experiments based on NLPCC 2018 Task 2 [7] demonstrate the robustness and effectiveness of our approach.