  The ability to capture complex linguistic structures and long-term
dependencies among words in the passage is essential for many natural language
understanding tasks. In relation extraction, dependency trees that contain rich
syntactic clues have been widely used to help capture long-term dependencies in
text. Graph neural networks (GNNs), one of the means to encode dependency
graphs, has been shown effective in several prior works. However, relatively
little attention has been paid to the receptive fields of GNNs, which can be
crucial in tasks with extremely long text that go beyond single sentences and
require discourse analysis. In this work, we leverage the idea of graph pooling
and propose the Mirror Graph Convolution Network (MrGCN), a GNN model with
pooling-unpooling structures tailored to relation extraction. The pooling
branch reduces the graph size and enables the GCN to obtain larger receptive
fields within less layers; the unpooling branch restores the pooled graph to
its original resolution such that token-level relation extraction can be
performed. Experiments on two datasets demonstrate the effectiveness of our
method, showing significant improvements over previous results.
