A Bi-consolidating Model for Joint Relational Triple Extraction
Xiaocheng Luo Yanping Chen Ruixue Tang Ruizhang Huang Yongbin Qin
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. It
is weak to learn discriminative semantic features relevant to a relation
triple. 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, it achieves
competitive performance. Analytical experiments demonstrate the effectiveness
of our model for relational triple extraction and give motivation for other
natural language processing tasks.