  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 and classification (which we call GREC), where RE
is modeled as a sequence-to-sequence generation task. We explore various
encoding representations for the source and target sequences, and design
effective schemes that enable GREC 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 the 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.
