Summary of Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling, by Himmet Toprak Kesgin et al.
Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling
by Himmet Toprak Kesgin, Mehmet Fatih Amasyali
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel text augmentation method for natural language processing (NLP) tasks, building upon the Fill-Mask feature of transformer-based BERT models. The approach involves iteratively masking words in a sentence and replacing them with predictions from the language model. Experimental results show that this method significantly improves performance on various NLP tasks, particularly topic classification datasets. The paper also compares its proposed method to existing augmentation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways to make machine learning models better at understanding text. Right now, there are many ways to improve computer vision models, but not as much for language processing. The researchers came up with a new idea that uses the Fill-Mask feature of BERT, a popular language model. They tested their method on different tasks and found it worked well, especially for classifying topics. This could be useful for all sorts of applications where understanding text is important. |
Keywords
* Artificial intelligence * Bert * Classification * Language model * Machine learning * Mask * Natural language processing * Nlp * Transformer
