Summary of Vaiyakarana : a Benchmark For Automatic Grammar Correction in Bangla, by Pramit Bhattacharyya and Arnab Bhattacharya
VAIYAKARANA : A Benchmark for Automatic Grammar Correction in Bangla
by Pramit Bhattacharyya, Arnab Bhattacharya
First submitted to arxiv on: 20 Jun 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 approach generates grammatically incorrect Bangla sentences systematically from a correct sentence by categorizing errors into 5 broad classes and 12 finer classes. This method addresses the lack of a large corpus for neural networks and mitigates the challenge of automatic grammar correction in Bangla. The study provides a dataset, Vaiyakarana, comprising 92,830 incorrect and 18,426 correct sentences, as well as 619 human-generated sentences from essays written by native speakers. Evaluation against neural models, LLMs, and human evaluators demonstrates that native speakers are more accurate in detecting grammatical correctness than state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps to solve a big problem: correcting grammar mistakes in the Bangla language. Right now, it’s hard to make computers understand and fix these errors because there aren’t many examples of incorrect sentences to train them on. The researchers came up with a new way to create lots of fake sentences that are grammatically wrong, which can help computers learn how to correct mistakes. They tested this approach by creating a big dataset of 92,000 incorrect and 18,000 correct Bangla sentences, as well as getting native speakers to write their own examples. The results show that people who speak Bangla are better at recognizing correct grammar than the best computer programs. |
