Summary of Targeted Augmentation For Low-resource Event Extraction, by Sijia Wang et al.
Targeted Augmentation for Low-Resource Event Extraction
by Sijia Wang, Lifu Huang
First submitted to arxiv on: 14 May 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 A novel approach to low-resource information extraction is introduced in this paper, tackling the challenge of limited training examples. The proposed paradigm employs targeted augmentation and back validation to create diverse, accurate, and coherent augmented examples. This addresses the existing imbalance between weak and drastic data augmentation methods. Experimental results demonstrate the effectiveness of the approach, highlighting its potential for improving information extraction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in computers: how to learn from very little data. Right now, it’s hard to get machines to understand what’s important when they only have a few examples to work with. The researchers came up with a new way to make more training data by using targeted tricks and checking that the new data makes sense. They tested their idea and showed that it works well. |
Keywords
» Artificial intelligence » Data augmentation
