Summary of Diva-docre: a Discriminative and Voice-aware Paradigm For Document-level Relation Extraction, by Yiheng Wu et al.
DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction
by Yiheng Wu, Roman Yangarber, Xian Mao
First submitted to arxiv on: 7 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 This paper introduces a novel approach to Document-level Relation Triplet Extraction (DocRTE), which involves identifying entities and their semantic relationships in documents. The existing methods are primarily designed for Sentence level Relation Triplet Extraction (SentRTE) and suffer from limitations such as handling limited relations and triplet facts within a single sentence, treating relations as candidate choices integrated into prompt templates, and inefficient processing. To address these issues, the authors propose a Discriminative and Voice Aware Paradigm DiVA, which involves two steps: document-level relation extraction (DocRE) and identifying subject-object entities based on the relation. The proposed method transforms DocRE into a discriminative task, paying attention to each relation and active vs. passive voice within the triplet. The authors demonstrate state-of-the-art results for the DocRTE task on the Re-DocRED and DocRED datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can better understand documents and find relationships between words and ideas. Currently, computers are great at reading single sentences but struggle when it comes to understanding longer documents. The authors come up with a new way to solve this problem by breaking it down into two steps: finding the main ideas in a document and then identifying what these ideas relate to each other. This approach is more accurate because it takes into account how words are used in different situations, like when something is happening versus someone else is doing it. |
Keywords
» Artificial intelligence » Attention » Prompt
