  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.
