# MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation
###### Abstract
This example shows that the CSC requires certain background information to detect and correct spelling errors. In the second example, the words \"late\" and \"equator\" are pronounced \"_chi_ dia_\" in Chinese, so that they are misused. By incorporating the Chinese context \"_shiang xue_\" (lit. 'go to school'), it can be deduced that the correct word should be \"late\". \begin{table}
\begin{tabular}{c c c} \hline \hline
**Wrong** & **Correct** & **Similar Pronunciation in Chinese** \\ \hline A elephant refers to a quantity & A vector refers to a quantity & \\ with magnitude and direction. & with magnitude and direction. & \\ \hline He is always equator for & He is always late for school. & _chi dao_ \\ school. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of Chinese spelling errors. Words added in red are incorrect and should be corrected to words added in green. Figure 1: The graph’s upper section illustrates the correct segmentation of words, while the lower section depicts incorrect word partitioning. The CSC models can be roughly divided into two categories: the traditional CSC models and those based on deep learning. The traditional CSC models share a similar pipeline scheme: first, use the word segmentation tool to segment the sentence, then replace the suspicious Chinese characters with the confusion set, and finally score the sentence via language models and select the sentence with the highest score as the CSC result. [PERSON] et al. [2] developed a hybrid CSC model, in which candidate characters for the misspelled character are generated first by using models based on statistical machine translation and language, and then the correct candidate character is screened out via SVM [3] classifier. Based on N-gram, traditional models tend to have limitations since the N-gram language model can only extract limited history information and cannot capture the future information. For the CSC models based on deep learning, [PERSON] et al. [4] proposed a sequence labeling-based method for Chinese spelling detection. Their spelling detection approach is innovative, but the feature vectors they adopted cannot adequately convey the relationship between the current characters and other characters in a sentence. There are a lot of work using the sequence-to-sequence models, and they achieved good performance. However, most of the sequence-to-sequence models based on Bert are faced with the problem of overcorrection. When using the form of \"error check first, then error correction\", the error correction model will obtain a priori information about the position of wrong characters. It can enable the model to avoid misjudgment that may be caused by context information. In this paper, we propose a novel CSC framework Based on Multi-label Annotation (MLSL-Spell) that divides the CSC task into two sub-tasks: spelling detection and spelling correction. First, the MLSL-Spell detection model takes Chinese characters as the unit, fuses the pre-trained context vectors and the Pinyin vector, and delivers them to the neural network model for sequence labeling. After fully learning the contextual information between the characters, the detection model outputs the labeled character sequence, which corresponds to the Chinese characters in the original sentence. If the current Chinese character is correct, it will be labeled as \"T\". If the current Chinese character is detected as an error of similar pronunciation, it will be labeled as \"P\"; if it is detected as an error of similar shape, it will be labeled as \"S\"; if the detected error belongs both to similar pronunciation and shape, it is labeled as \"B\"; if it is an error of other types, it will be labeled as \"O\". As for the spelling correction module of MLSL-Spell, it receives the character sequence output from the spelling detection module, and corrects the misspelled Chinese characters that are detected.