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.

翻译：虽然事实证明注意机制在许多国家劳工规划任务中是有效的,但其中大多数是数据驱动机制,我们提议建立一个新的知识关注编码器,将外部词汇资源先前的知识纳入深神经网络,以开展关系提取任务;此外,我们提出三种有效的方法,将知识关注与自我关注结合起来,以最大限度地利用知识和数据;拟议的关系提取系统是端对端的,完全以关注为基础;实验结果显示,拟议的知识关注机制与自我关注具有互补优势,我们的综合模型优于现有的CNN、RNN和以自我关注为基础的模型;在TRACRED这一复杂和大规模的关系提取数据集上取得了最先进的业绩。