  Tagging based relational triple extraction methods are attracting growing
research attention recently. However, most of these methods take a
unidirectional extraction framework that first extracts all subjects and then
extracts objects and relations simultaneously based on the subjects extracted.
This framework has an obvious deficiency that it is too sensitive to the
extraction results of subjects. To overcome this deficiency, we propose a
bidirectional extraction framework based method that extracts triples based on
the entity pairs extracted from two complementary directions. Concretely, we
first extract all possible subject-object pairs from two paralleled directions.
These two extraction directions are connected by a shared encoder component,
thus the extraction features from one direction can flow to another direction
and vice versa. By this way, the extractions of two directions can boost and
complement each other. Next, we assign all possible relations for each entity
pair by a biaffine model. During training, we observe that the share structure
will lead to a convergence rate inconsistency issue which is harmful to
performance. So we propose a share-aware learning mechanism to address it. We
evaluate the proposed model on multiple benchmark datasets. Extensive
experimental results show that the proposed model is very effective and it
achieves state-of-the-art results on all of these datasets. Moreover,
experiments show that both the proposed bidirectional extraction framework and
the share-aware learning mechanism have good adaptability and can be used to
improve the performance of other tagging based methods. The source code of our
work is available at: https://github.com/neukg/BiRTE.
