  This paper explores how the Distantly Supervised Relation Extraction (DS-RE)
can benefit from the use of a Universal Graph (UG), the combination of a
Knowledge Graph (KG) and a large-scale text collection. A straightforward
extension of a current state-of-the-art neural model for DS-RE with a UG may
lead to degradation in performance. We first report that this degradation is
associated with the difficulty in learning a UG and then propose two training
strategies: (1) Path Type Adaptive Pretraining, which sequentially trains the
model with different types of UG paths so as to prevent the reliance on a
single type of UG path; and (2) Complexity Ranking Guided Attention mechanism,
which restricts the attention span according to the complexity of a UG path so
as to force the model to extract features not only from simple UG paths but
also from complex ones. Experimental results on both biomedical and NYT10
datasets prove the robustness of our methods and achieve a new state-of-the-art
result on the NYT10 dataset. The code and datasets used in this paper are
available at https://github.com/baodaiqin/UGDSRE.
