Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next
원문 초록
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 to distinguish misspelled characters, with good results. However, the generalization ability of these models is not 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.
함께 살펴볼 논문
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C-LLM: Learn to Check Chinese Spelling Errors Character by Character
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
Block the Label and Noise: An N-Gram Masked Speller for Chinese Spell Checking
SDCL: Self-Distillation Contrastive Learning for Chinese Spell Checking
토론
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