  Current methods to extract relational triples directly make a prediction
based on a possible entity pair in a raw sentence without depending on entity
recognition. The task suffers from a serious semantic overlapping problem, in
which several relation triples may share one or two entities in a sentence. In
this paper, based on a two-dimensional sentence representation, a
bi-consolidating model is proposed to address this problem by simultaneously
reinforcing the local and global semantic features relevant to a relation
triple. This model consists of a local consolidation component and a global
consolidation component. The first component uses a pixel difference
convolution to enhance semantic information of a possible triple representation
from adjacent regions and mitigate noise in neighbouring neighbours. The second
component strengthens the triple representation based a channel attention and a
spatial attention, which has the advantage to learn remote semantic
dependencies in a sentence. They are helpful to improve the performance of both
entity identification and relation type classification in relation triple
extraction. After evaluated on several publish datasets, the bi-consolidating
model achieves competitive performance. Analytical experiments demonstrate the
effectiveness of our model for relational triple extraction and give motivation
for other natural language processing tasks.
