# MLSL-Spell: Chinese Spelling Check Based on Multi-Label Annotation
###### Abstract  
Chinese spelling errors are commonplace in our daily lives, which might be caused by input methods, optical character recognition, or speech recognition. Due to Chinese characters' phonetic and visual similarities, the Chinese spelling check (CSC) is a very challenging task. However, the existing CSC solutions cannot achieve good spelling check performance since they often fail to fully extract the contextual information and Pinyin information. In this paper, we propose a novel CSC framework based on multi-label annotation (MLSL-Spell), consisting of two basic phases: spelling detection and correction. In the spelling detection phase, MLSL-Spell uses the fusion vectors of both character-based pre-trained context vectors and Pinyin vectors and adopts the sequence labeling method to explicitly label the type of misspelled characters. In the spelling correction phase, MLSL-Spell uses Masked Language Mode (MLM) model to generate candidate characters, then performs corresponding screenings according to the error types, and finally screens out the correct characters through the XGBoost classifier. Experiments show that the MLSL-Spell model outperforms the benchmark model. On SIGHAN 2013 dataset, the spelling detection F1 score of MLSL-Spell is 18.3% higher than that of the pointer network (PN) model, and the spelling correction F1 score is 10.9% higher. On SIGHAN 2015 dataset, the spelling detection F1 score of MLSL-Spell is 11% higher than that of Bert and 15.7% higher than that of the PN model. And the spelling correction F1 of MLSL-Spell score is 6.8% higher than that of PN model. Chinese spelling check (CSC); sequence labeling; Masked Language Model (MLM); XGBoost +
Footnote †: journal: Medical Image and Vision  
0000-0001-939X/0  
we must employ a combination of two Chinese characters. The former pronunciation is \"_zhong_\" which means'middle', and the latter pronunciation is \"_guo_\" which means 'country'. If feature vectors based on character granularity are used, the relationship between the current character and other characters in the sentence cannot be fully extracted. While the semantic integrity of words can be ensured by using word granularity-based feature vector extraction, it involves text segmentation, which is prone to errors. For example, the Chinese phrase \"_y_i_e_t_an_z_hen_de_hai_zi_\" translates to 'an innocent child' in English. This phrase conveys the idea of a child who is pure and without guilt. More specifically, the Chinese term \"_y_i_e_\" translates to 'an' in English, while \"_tian zhen de_\" and \"_hai zi_\" translate to 'innocent' and 'child', respectively. However, if the Chinese word \"_tian zhen de_\" is split into \"_tian_\" and \"_zhen de_\", their English translations are'sky' and 'true' respectively. This can result in a meaning that is significantly different from the original sentence. As shown in Figure 1, if this Chinese phrase is segmented incorrectly as \"_y_i_e_/_tian_/_zhen_de_/_hai_zi_\" (lit. 'an/sky/true/child'), the resulting sentence will be semantically unintelligible and difficult to understand. Therefore, \"_tian zhen de_\" (meaning 'innocent') should be used as an inseparable phrasal adjective. If training is carried out with the incorrect word segmentation results, the semantic output of the word vectors after training will have a certain deviation, which will, to some extent, influence the CSC task. Therefore, Chinese spelling detection is pivot to Chinese spelling correction. For the detection phase, it is significantly more difficult to detect spelling errors at the character level than it is at the sentence and phrase level. Furthermore, the CSC requires some background information and contextual reasoning ability to obtain a desirable spelling correction performance. Two sets of examples are shown in Table 1. In the first example, the word \"vector\" is a mathematical term that is wrongly transcribed as \"elephant\" because they have similar pronunciations \"_xiang_\" in Chinese.