Latent graph learning with dual-channel attention for relation extraction

As a building block of information retrieval, relation extraction aims at predicting the relation type between two given entities in a piece of text. This task becomes challenging when it is confronted with long text that contains many task-unrelated tokens. Recent attempts to solve this problem have resorted to learning the relatedness among tokens. However, how to obtain appropriate graph for better relatedness representation still remains outstanding, while existing methods have room to improve. In this paper, we propose a novel latent graph learning method to enhance the expressivity of contextual information for the entities of interest. In particular, we design a dual-channel attention mechanism for multi-view graph learning and pool the learned multi-views to sift unrelated tokens for latent graph. This process can be repeated many times for refining the latent structure. We show that our method achieves superior performance on several benchmark datasets, compared to strong baseline models and prior multi-view graph learning approach.