  Distant Supervision for Relation Extraction uses heuristically aligned text
data with an existing knowledge base as training data. The unsupervised nature
of this technique allows it to scale to web-scale relation extraction tasks, at
the expense of noise in the training data. Previous work has explored
relationships among instances of the same entity-pair to reduce this noise, but
relationships among instances across entity-pairs have not been fully
exploited. We explore the use of inter-instance couplings based on verb-phrase
and entity type similarities. We propose a novel technique, CANDiS, which casts
distant supervision using inter-instance coupling into an end-to-end neural
network model. CANDiS incorporates an attention module at the instance-level to
model the multi-instance nature of this problem. CANDiS outperforms existing
state-of-the-art techniques on a standard benchmark dataset.
