While attention mechanisms have been proven to be effective in many NLP
tasks, majority of them are data-driven. We propose a novel knowledge-attention
encoder which incorporates prior knowledge from external lexical resources into
deep neural networks for relation extraction task. Furthermore, we present
three effective ways of integrating knowledge-attention with self-attention to
maximize the utilization of both knowledge and data. The proposed relation
extraction system is end-to-end and fully attention-based. Experiment results
show that the proposed knowledge-attention mechanism has complementary
strengths with self-attention, and our integrated models outperform existing
CNN, RNN, and self-attention based models. State-of-the-art performance is
achieved on TACRED, a complex and large-scale relation extraction dataset.

本文提出了一种新颖的知识 - 注意力编码器，并介绍了三种将知识 - 注意力与自注意力相结合的有效方法。提出的关系提取系统是端到端和完全基于注意力的，实验证明知识 - 注意力机制与自注意力相互补充，在 TACRED 上实现了最先进的性能，并且胜过了现有的 CNN、RNN 和自注意力模型。