  We cast a suite of information extraction tasks into a text-to-triple
translation framework. Instead of solving each task relying on task-specific
datasets and models, we formalize the task as a translation between
task-specific input text and output triples. By taking the task-specific input,
we enable a task-agnostic translation by leveraging the latent knowledge that a
pre-trained language model has about the task. We further demonstrate that a
simple pre-training task of predicting which relational information corresponds
to which input text is an effective way to produce task-specific outputs. This
enables the zero-shot transfer of our framework to downstream tasks. We study
the zero-shot performance of this framework on open information extraction
(OIE2016, NYT, WEB, PENN), relation classification (FewRel and TACRED), and
factual probe (Google-RE and T-REx). The model transfers non-trivially to most
tasks and is often competitive with a fully supervised method without the need
for any task-specific training. For instance, we significantly outperform the
F1 score of the supervised open information extraction without needing to use
its training set.
