Summary of Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using Chatgpt For Arabic Grammatical Error Correction, by Ahlam Alrehili et al.
Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction
by Ahlam Alrehili, Areej Alhothali
First submitted to arxiv on: 7 Nov 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 study proposes an Arabic language processing approach that leverages text data augmentation to overcome sample size constraints in grammatical error correction (GEC). The researchers chose Arabic as it has limited resources for GEC, and existing datasets like QALB-14 and QALB-15 have only 20,500 parallel examples. To address this, they developed the “Tibyan” corpus using ChatGPT as a data augmenter tool. This corpus is created by pairing sentences with grammatical errors with error-free guide sentences from Arabic books. The corpus was validated and refined through iterative feedback from linguistic experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to improve computers’ ability to correct mistakes in Arabic writing. Currently, there aren’t many resources available for fixing Arabic grammar errors. The researchers used a tool called ChatGPT to create a big collection of sentence pairs with errors and correct sentences. They then had experts check the automatically generated sentences to make sure they were accurate. This new corpus has 600,000 tokens and can help improve computer systems’ ability to understand and correct Arabic writing. |
Keywords
» Artificial intelligence » Data augmentation
