论文标题
通过贝叶斯元学习在关系图上进行的几个射击关系提取
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
论文作者
论文摘要
本文研究了很少的射击关系提取，旨在通过训练中有几个标记的示例来预测一对句子中一对实体的关系。为了更有效地推广到新的关系，在本文中，我们研究了不同关系之间的关系，并建议利用全球关系图。我们提出了一种新型的贝叶斯元学习方法，以有效地学习关系的原型向量的后验分布，其中原型向量的初始先验是用全局关系图上的图神经网络参数化的。此外，为了有效地优化原型矢量的后验分布，我们建议使用与MAML算法有关的随机梯度Langevin Dynamics，但能够处理原型矢量的不确定性。整个框架可以以端到端的方式有效，有效地优化。在两个基准数据集上的实验证明了我们在少数拍摄和零射击设置中针对竞争基准的方法的有效性。
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.
