  Attention mechanisms are often used in deep neural networks for distantly
supervised relation extraction (DS-RE) to distinguish valid from noisy
instances. However, traditional 1-D vector attention models are insufficient
for the learning of different contexts in the selection of valid instances to
predict the relationship for an entity pair. To alleviate this issue, we
propose a novel multi-level structured (2-D matrix) self-attention mechanism
for DS-RE in a multi-instance learning (MIL) framework using bidirectional
recurrent neural networks. In the proposed method, a structured word-level
self-attention mechanism learns a 2-D matrix where each row vector represents a
weight distribution for different aspects of an instance regarding two
entities. Targeting the MIL issue, the structured sentence-level attention
learns a 2-D matrix where each row vector represents a weight distribution on
selection of different valid in-stances. Experiments conducted on two publicly
available DS-RE datasets show that the proposed framework with a multi-level
structured self-attention mechanism significantly outperform state-of-the-art
baselines in terms of PR curves, P@N and F1 measures.
