Clustering-Augmented Multi-instance Learning for Neural Relation Extraction

Abstract

Despite its efficiency in generating training data, distant supervision for sentential relation extraction assigns labels to instances in a context-agnostic manner—a process that may introduce false labels and confuse sentential model learning. In this paper, we propose to integrate instance clustering with distant training, and develop a novel clustering-augmented multi-instance training framework. Specifically, for sentences labeled with the same relation type, we jointly perform clustering based on their semantic representations, and treat each cluster as a training unit for multi-instance training. Comparing to existing bag-level attention models, our proposed method does not restrict the training unit to be sentences with the same entity pair, as it may cause the selective attention to focus on instances with simple sentence context, and thus fail to provide informative supervision. Experiments on two popular datasets demonstrate the effectiveness of augmenting multi-instance learning with clustering.