  Recent years have seen rapid progress in identifying predefined relationship
between entity pairs using neural networks NNs. However, such models often make
predictions for each entity pair individually, thus often fail to solve the
inconsistency among different predictions, which can be characterized by
discrete relation constraints. These constraints are often defined over
combinations of entity-relation-entity triples, since there often lack of
explicitly well-defined type and cardinality requirements for the relations. In
this paper, we propose a unified framework to integrate relation constraints
with NNs by introducing a new loss term, ConstraintLoss. Particularly, we
develop two efficient methods to capture how well the local predictions from
multiple instance pairs satisfy the relation constraints. Experiments on both
English and Chinese datasets show that our approach can help NNs learn from
discrete relation constraints to reduce inconsistency among local predictions,
and outperform popular neural relation extraction NRE models even enhanced with
extra post-processing. Our source code and datasets will be released at
https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.
