Summary of Disc: Plug-and-play Decoding Intervention with Similarity Of Characters For Chinese Spelling Check, by Ziheng Qiao et al.
DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check
by Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DISC module is a lightweight plug-and-play solution for improving Chinese spelling check (CSC) tasks. The challenge in CSC lies in incorrect characters being phonetically or glyph-wise similar to correct ones, which previous works addressed using confusion sets. However, this approach has limitations, such as difficulties in determining character pairs and lack of probabilities to distinguish items. To overcome these issues, the DISC module measures phonetic and glyph similarities between characters and incorporates this information during inference only. This method can be easily integrated into existing CSC models without additional training costs. Experimental results on three benchmarks demonstrate significant improvements in model performance, even surpassing state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed DISC module is a simple way to make Chinese spelling check (CSC) better. The problem with CSC is that incorrect characters are often very similar to the correct ones. Previous attempts to solve this issue have some limitations. A new method measures how similar characters are and uses this information when making predictions. This new method can be easily added to existing CSC models without requiring more training data. The results show that this approach can make existing models better, even surpassing the best current models. |
Keywords
» Artificial intelligence » Inference
