A Simple, Strong and Robust Baseline for Distantly Supervised Relation Extraction

• URL: http://arxiv.org/abs/2110.07415v1
• Date: Thu, 14 Oct 2021 14:45:51 GMT
• Title: A Simple, Strong and Robust Baseline for Distantly Supervised Relation Extraction
• Abstract summary: We come up with a novel aggregation scheme which we call Passage-Att. Under this aggregation scheme, we combine all instances mentioning an entity pair into a "passage of instances" These summaries are used to predict the validity of a potential triple. We show that our Passage-Att with BERT as passage encoder achieves state-of-the-art performance in three different settings.
• Score: 19.146415895872956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distantly supervised relation extraction (DS-RE) is generally framed as a multi-instance multi-label (MI-ML) task, where the optimal aggregation of information from multiple instances is of key importance. Intra-bag attention (Lin et al., 2016) is an example of a popularly used aggregation scheme for this framework. Apart from this scheme, however, there is not much to choose from in the DS-RE literature as most of the advances in this field are focused on improving the instance-encoding step rather than the instance-aggregation step. With recent works leveraging large pre-trained language models as encoders, the increased capacity of models might allow for more flexibility in the instance-aggregation step. In this work, we explore this hypothesis and come up with a novel aggregation scheme which we call Passage-Att. Under this aggregation scheme, we combine all instances mentioning an entity pair into a "passage of instances", which is summarized independently for each relation class. These summaries are used to predict the validity of a potential triple. We show that our Passage-Att with BERT as passage encoder achieves state-of-the-art performance in three different settings (monolingual DS, monolingual DS with manually-annotated test set, multilingual DS).

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