Dependency trees convey rich structural information that is proven useful for
extracting relations among entities in text. However, how to effectively make
use of relevant information while ignoring irrelevant information from the
dependency trees remains a challenging research question. Existing approaches
employing rule based hard-pruning strategies for selecting relevant partial
dependency structures may not always yield optimal results. In this work, we
propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model
which directly takes full dependency trees as inputs. Our model can be
understood as a soft-pruning approach that automatically learns how to
selectively attend to the relevant sub-structures useful for the relation
extraction task. Extensive results on various tasks including cross-sentence
n-ary relation extraction and large-scale sentence-level relation extraction
show that our model is able to better leverage the structural information of
the full dependency trees, giving significantly better results than previous
approaches.

本研究提出了一种新模型 ——AGGCNs，该模型通过自动学习选择性地关注依存树的相关子结构，有效地利用依存树的结构信息来提取实体之间的关系，从而在交叉句 n 元关系抽取和大规模句子级关系抽取等多项任务中均优于现有方法。