Summary of Enhancing Grammatical Error Detection Using Bert with Cleaned Lang-8 Dataset, by Rahul Nihalani et al.
Enhancing Grammatical Error Detection using BERT with Cleaned Lang-8 Dataset
by Rahul Nihalani, Kushal Shah
First submitted to arxiv on: 23 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 paper presents an improved Large Language Model (LLM) based model for Grammatical Error Detection (GED), a challenging problem with important implications for many applications. Building on recent advances in Neural Networks (NN), this study fine-tunes transformer models using the Lang8 dataset, rigorously cleaned by the authors. The BERT-base-uncased model achieves impressive performance with an F1 score of 0.91 and accuracy of 98.49% on training data, and 90.53% on testing data. Notably, larger models do not necessarily lead to better performance, highlighting the importance of data cleaning in this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a machine that can correct mistakes in writing. The old way was to create rules by hand, but now computers can learn from examples and make their own rules. This makes it more accurate. The best model they tried was called BERT, which got 91% of the answers right. It also showed that sometimes using a bigger computer doesn’t always help. Clean data is important for this task. |
Keywords
» Artificial intelligence » Bert » F1 score » Large language model » Transformer
