Summary of Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification, by Shayekh Bin Islam et al.
Bangla Grammatical Error Detection Leveraging Transformer-based Token Classification
by Shayekh Bin Islam, Ridwanul Hasan Tanvir, Sihat Afnan
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The proposed paper tackles the understudied problem of developing an automated grammar checker for the Bangla language, crucial for creating an efficient typing assistant. The task is framed as a token classification problem and leveraged transformer-based models to achieve this goal. The output of these models is combined and processed using rule-based methods to produce a more reliable result. Evaluation is conducted on a dataset consisting of over 25,000 texts from various sources, with the best model achieving a Levenshtein distance score of 1.04. A detailed analysis of system components is also provided. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary An automated grammar checker for Bangla is important because it can help people type more accurately. Currently, there isn’t much research on this topic. The researchers broke down the problem into smaller parts and used special language models to solve it. They combined the results and did some extra work to make sure it was accurate. They tested their system on a large dataset of over 25,000 texts and got good results. |
Keywords
* Artificial intelligence * Classification * Token * Transformer
