ReadsRE: Retrieval-Augmented Distantly Supervised Relation Extraction

Abstract

Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). To address the noisy label problem, most models have adopted the multi-instance learning paradigm by representing entity pairs as a bag of sentences. However, this strategy depends on multiple assumptions (e.g., all sentences in a bag share the same relation), which may be invalid in real-world applications. Besides, it cannot work well on long-tail entity pairs which have few supporting sentences in the dataset. In this work, we propose a new paradigm named retrieval-augmented distantly supervised relation extraction (ReadsRE), which can incorporate large-scale open-domain knowledge (e.g., Wikipedia) into the retrieval step. ReadsRE seamlessly integrates a neural retriever and a relation predictor in an end-to-end framework. We demonstrate the effectiveness of ReadsRE on the well-known NYT10 dataset. The experimental results verify that ReadsRE can effectively retrieve meaningful sentences (i.e., denoise), and relieve the problem of long-tail entity pairs in the original dataset through incorporating external open-domain corpus. Through comparisons, we show ReadsRE outperforms other baselines for this task.