Name
Abstract | ||
|---|---|---|
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the relation between one entity pair in a document. In this paper, we propose a novel discriminative reasoning framework to explicitly model the paths of these reasoning skills between each entity pair in this document. Thus, a discriminative reasoning network is designed to estimate the relation probability distribution of different reasoning paths based on the constructed graph and vectorized document contexts for each entity pair, thereby recognizing their relation. Experimental results show that our method outperforms the previous state-of-the-art performance on the large-scale DocRE dataset. The code is publicly available at https://github.com/xwjim/DRN. |
Year | Venue | DocType |
|---|---|---|
2021 | ACL/IJCNLP | Conference |
Volume | Citations | PageRank |
2021.findings-acl | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang Xu | 1 | 0 | 1.01 |
| Kehai Chen | 2 | 43 | 16.34 |
| Tiejun Zhao | 3 | 643 | 102.68 |