Summary of Edacsc: Two Easy Data Augmentation Methods For Chinese Spelling Correction, by Lei Sheng and Shuai-shuai Xu
EdaCSC: Two Easy Data Augmentation Methods for Chinese Spelling Correction
by Lei Sheng, Shuai-Shuai Xu
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes two data augmentation methods to improve Chinese Spelling Correction (CSC) models in detecting and correcting spelling errors caused by phonetic or visual similarities. The current CSC models integrate pinyin or glyph features, but they still struggle with sentences containing multiple typos and are prone to overcorrection. To address these limitations, the authors propose data augmentation methods that either split long sentences into shorter ones or reduce typos in sentences with multiple errors. They then employ different training processes to select the optimal model. The experimental results on SIGHAN benchmarks show that their approach outperforms most existing models, achieving state-of-the-art performance on the SIGHAN15 test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix mistakes in Chinese writing by using new ways to make a dataset of correct and incorrect sentences. The current systems are good at correcting simple mistakes, but they struggle when there are many mistakes in one sentence. To make it better, the researchers came up with two new ideas for adding more data to the training set. One idea is to break long sentences into shorter ones, and the other idea is to fix some of the mistakes in sentences that have multiple errors. They then tried different ways to train the model to find the best one. The results show that their approach works better than most others, even beating the best one on a special test set. |
Keywords
* Artificial intelligence * Data augmentation
