Improving Relation Extraction with Knowledge-attention
This work addresses relation extraction for NLP applications, offering an incremental improvement by integrating knowledge-attention with self-attention.
The paper tackles relation extraction by incorporating prior knowledge from external lexical resources into deep neural networks using a knowledge-attention encoder, achieving state-of-the-art performance on the TACRED dataset.
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.