Summary of Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin, by Tianlin Guo et al.
Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin
by Tianlin Guo, Lingling Zhang, Jiaxin Wang, Yuokuo Lei, Yifei Li, Haofen Wang, Jun Liu
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 Few-shot relation extraction with none-of-the-above (FsRE with NOTA) is a challenging task that predicts labels in few-shot scenarios with unknown classes. Prototype-based methods are commonly used, but their performance is limited due to few-shot overfitting and boundary confusion between known and unknown classes. To address this issue, we propose a novel framework, GPAM (Gaussian prototype and adaptive margin), which consists of three modules: semi-factual representation, GMM-prototype metric learning, and decision boundary learning. GPAM utilizes contrastive learning loss to stabilize the model and achieve robustness. Our experiments on the FewRel dataset demonstrate that GPAM outperforms previous prototype methods and achieves state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand relationships between things with very little information. That’s what few-shot relation extraction is all about! It’s a tough task because we don’t know much about the unknown categories. Most solutions use a type of machine learning called prototype-based methods, but they can get stuck and make mistakes. To fix this, our team created a new approach called GPAM (Gaussian prototype and adaptive margin). We split it into three parts to help the model learn better representations, distances between classes, and boundaries. Our method uses special training techniques to keep the model stable and accurate. In experiments on a dataset called FewRel, we showed that GPAM does better than previous methods! |
Keywords
» Artificial intelligence » Few shot » Machine learning » Overfitting
