Summary of Efficient Information Extraction in Few-shot Relation Classification Through Contrastive Representation Learning, by Philipp Borchert et al.
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
by Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens
First submitted to arxiv on: 25 Mar 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 In this paper, researchers tackle the challenge of identifying relationships between entities with limited labeled data. They propose a new approach that combines multiple sentence representations using contrastive learning to extract more information from text. The method aligns various representation types, including the [CLS] token and entity marker tokens, to distill discriminative features for relation classification. This approach is particularly effective in low-resource settings where data is scarce. The authors validate their method’s adaptability and flexibility across different scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand text relationships by combining multiple ways of representing sentences. It does this by using a technique called contrastive learning, which helps the computer learn more from the text. The approach is useful when there isn’t much labeled data available. The researchers show that their method works well even when some additional information is available. |
Keywords
» Artificial intelligence » Classification » Token
