  Relation extraction is an important task in structuring content of text data,
and becomes especially challenging when learning with weak supervision---where
only a limited number of labeled sentences are given and a large number of
unlabeled sentences are available. Most existing work exploits unlabeled data
based on the ideas of self-training (i.e., bootstrapping a model) and
multi-view learning (e.g., ensembling multiple model variants). However, these
methods either suffer from the issue of semantic drift, or do not fully capture
the problem characteristics of relation extraction. In this paper, we leverage
a key insight that retrieving sentences expressing a relation is a dual task of
predicting relation label for a given sentence---two tasks are complementary to
each other and can be optimized jointly for mutual enhancement. To model this
intuition, we propose DualRE, a principled framework that introduces a
retrieval module which is jointly trained with the original relation prediction
module. In this way, high-quality samples selected by retrieval module from
unlabeled data can be used to improve prediction module, and vice versa.
Experimental results\footnote{\small Code and data can be found at
\url{https://github.com/INK-USC/DualRE}.} on two public datasets as well as
case studies demonstrate the effectiveness of the DualRE approach.
