RTF: Region-based Table Filling Method for Relational Triple Extraction
This work improves relational triple extraction for knowledge graph construction, but it is incremental as it builds on existing methods by focusing on spatial dependencies.
The paper tackles the problem of relational triple extraction for knowledge graph construction by addressing the weakness in entity pair boundary detection due to ignored local spatial dependencies, and proposes a Region-based Table Filling method that achieves state-of-the-art results with better generalization on benchmark datasets.
Relational triple extraction is crucial work for the automatic construction of knowledge graphs. Existing methods only construct shallow representations from a token or token pair-level. However, previous works ignore local spatial dependencies of relational triples, resulting in a weakness of entity pair boundary detection. To tackle this problem, we propose a novel Region-based Table Filling method (RTF). We devise a novel region-based tagging scheme and bi-directional decoding strategy, which regard each relational triple as a region on the relation-specific table, and identifies triples by determining two endpoints of each region. We also introduce convolution to construct region-level table representations from a spatial perspective which makes triples easier to be captured. In addition, we share partial tagging scores among different relations to improve learning efficiency of relation classifier. Experimental results show that our method achieves state-of-the-art with better generalization capability on three variants of two widely used benchmark datasets.