GenRE: A Generative Model for Relation Extraction

Anonymous

Abstract: Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering. In this paper, we present a novel generative model for relation extraction (which we call GenRE), where RE is modeled as a sequence-to-sequence generation task. We explore various encoding schemes for the source and target sequences, and design effective schemes that enable GenRE to achieve state-of-the-art performance on three benchmark RE datasets. In addition, we introduce negative sampling and decoding scaling techniques which provide a flexible tool to tune the precision and recall performance of our GenRE model. Our approach can be extended to extract all relation triples from a sentence in one pass. Although the one-pass approach incurs certain performance loss, it is much more computationally efficient.