Investigating Glyph-Phonetic Information for C hinese Spell Checking: What Works and What’s Next? (2023)

Abstract: While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge. Previous research has explored using information such as glyphs and phonetics to improve the ability of CSC models to distinguish misspelled characters, with good results at the accuracy level on public datasets. However, the generalization ability of these CSC models has not been well understood: it is unclear whether they incorporate glyph-phonetic information and, if so, whether this information is fully utilized. In this paper, we aim to better understand the role of glyph-phonetic information in the CSC task and suggest directions for improvement. Additionally, we propose a new, more challenging, and practical setting for testing the generalizability of CSC models. All code is made publicly available.

## introduction spell checking (sc) is the process of detecting and correcting spelling errors in natural human texts. for some languages, such as english, sc is relatively straightforward, thanks to the use of tools like the levenshtein distance and a well-defined vocabulary. however, for chinese, chinese spell checking (csc) is a more challenging task, due to the nature of the chinese language. chinese has a large vocabulary consisting of at least 3,500 common characters, which creates a vast search space and an unbalanced distribution of errors @xcite . moreover, substitutions or combinations of characters can significantly alter the meaning of a chinese sentence while still being grammatically correct. the csc task, therefore, requires requires the output to retain as much of the original meaning and wording as possible. figure 1 shows different types of errors and corresponding target characters. previous work has attempted to incorporate inductive bias to model the relationship between chinese character glyphs, pronunciation, and semantics @xcite . in recent years, pre-trained language models (plms) have shown great success in a wide range of nlp tasks. with the publication of bert @xcite , using plms for csc tasks has become a mainstream approach, with examples including faspell @xcite , softmasked-bert @xcite , spellgcn @xcite , and plome @xcite . some researchers have focused on the special features of chinese characters in terms of glyphs and pronunciations, aiming to improve the ability to distinguish misspelled characters by incorporating glyphphonetic information @xcite @xcite . however, despite these advances, the generalization of csc models to realworld applications remains limited. how can we improve the generalization ability of csc models? can current models recognize and utilize glyphphonetic information to make predictions? as we re-examine previous work, we have identified some previously unexplored issues and potential future directions for research. q1: do existing chinese pre-trained models encode the glyph-phonetic information of chinese characters? chinese writing is morpho-semantic, and its characters contain additional semantic information. before studying existing csc models, we seek to investigate whether existing mainstream chinese pre-trained language models are capable of capturing the glyph-phonetic information. q2: do existing csc models fully utilize the glyph-phonetic information of misspelled characters to make predictions? intuitively, introducing glyph-phonetic information in the csc task can help identify misspelled characters and improve the performance of the model. however, there has been little research on whether existing csc models effectively use glyph-phonetic information in this way. empirically, our main observations are summarized as follows: we hope that this detailed empirical study will provide follow-up researchers with more guidance on how to better incorporate glyph-phonetic information in csc tasks and pave the way for new state-of-the-art results in this area. ## experiment-i: probing for character glyph-phonetic information in this section, we conduct a simple mlp-based probe to explore the presence of glyph and phonetic information in chinese plms and to quantify the extent to which tokens capture glyph-phonetic information. we consider glyph and phonetic information separately in this experiment. ## experiment-ii: probing for homonym correction in this experiment, we aim to explore the extent to which existing models can make use of the information from misspelled characters. to do this, we propose a new probe called correction with misspelled character coverage ratio(cccr), which investigates whether the model can adjust its prediction probability distribution based on the glyph-phonetic information of misspelled characters when making predictions. ## conclusion in this paper, we have explored the role of glyphphonetic information from misspelled characters in chinese spell checking (csc). based on our experimental results, we have reached the following conclusions: our detailed observations can provide valuable insights for future research in this field. it is clear that a more explicit treatment of glyph-phonetic information is necessary, and researchers should consider how to fully utilize this information to improve the generalizability of their csc models. we welcome follow-up researchers to verify the generalizability of their models using our proposed new setting.