Summary of Augmenting Document-level Relation Extraction with Efficient Multi-supervision, by Xiangyu Lin et al.
Augmenting Document-level Relation Extraction with Efficient Multi-Supervision
by Xiangyu Lin, Weijia Jia, Zhiguo Gong
First submitted to arxiv on: 1 Jul 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 A novel approach is proposed to efficiently utilize distantly supervised data for document-level relation extraction, which has been largely underexplored despite its popularity in sentence-level tasks. Existing work typically uses distantly supervised data as a whole, resulting in low time efficiency. The proposed method, Efficient Multi-Supervision, addresses this issue by selecting a subset of informative documents from the massive dataset using a combination of distant and expert supervision. This is achieved through a Multi-Supervision Ranking Loss that integrates knowledge from multiple sources of supervision to alleviate noise effects. Experimental results demonstrate improved model performance with higher time efficiency compared to existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Distantly supervised data has been widely used for sentence-level relation extraction, but its potential in document-level relation extraction remains untapped due to the noisy nature and low information density of this type of data. The current applications of distantly supervised data are mostly limited to pertaining, which is a time-consuming task. To address this issue, researchers have proposed a new method called Efficient Multi-Supervision that selects a subset of informative documents from the massive dataset using both distant and expert supervision. This approach has been shown to improve model performance while reducing time efficiency. |
Keywords
» Artificial intelligence » Supervised
