  This paper studies few-shot relation extraction, which aims at predicting the
relation for a pair of entities in a sentence by training with a few labeled
examples in each relation. To more effectively generalize to new relations, in
this paper we study the relationships between different relations and propose
to leverage a global relation graph. We propose a novel Bayesian meta-learning
approach to effectively learn the posterior distribution of the prototype
vectors of relations, where the initial prior of the prototype vectors is
parameterized with a graph neural network on the global relation graph.
Moreover, to effectively optimize the posterior distribution of the prototype
vectors, we propose to use the stochastic gradient Langevin dynamics, which is
related to the MAML algorithm but is able to handle the uncertainty of the
prototype vectors. The whole framework can be effectively and efficiently
optimized in an end-to-end fashion. Experiments on two benchmark datasets prove
the effectiveness of our proposed approach against competitive baselines in
both the few-shot and zero-shot settings.
