Summary of Fusion Makes Perfection: An Efficient Multi-grained Matching Approach For Zero-shot Relation Extraction, by Shilong Li et al.
Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extraction
by Shilong Li, Ge Bai, Zhang Zhang, Ying Liu, Chenji Lu, Daichi Guo, Ruifang Liu, Yong Sun
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 predicting unseen relations in relation extraction, a task that requires matching semantics between input instances and label descriptions. Previous approaches have made progress by fine-tuning their methods for specific tasks, but these methods often require laborious manual annotation and rich interactions between instances and label descriptions, which can be computationally expensive. The authors propose an efficient multi-grained matching approach that reduces manual annotation cost using virtual entity matching and fuses coarse-grained recall with fine-grained classification to achieve guaranteed inference speed. Experimental results show that this approach outperforms previous State Of The Art (SOTA) methods in zero-shot relation extraction tasks, striking a balance between inference efficiency and prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to understand relationships between things. Right now, it’s hard for computers to predict new relationships they haven’t seen before. Researchers have been trying to make this process better by matching the meanings of words and phrases. But this has its own problems – it requires a lot of work and takes up lots of computer power. The authors of this paper came up with a new way to do this that is faster and more efficient, while still being very accurate. This could help computers understand relationships even better, which would be really useful for many areas like artificial intelligence. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Inference » Recall » Semantics » Zero shot
