  The task of triplet extraction aims to extract pairs of entities and their
corresponding relations from unstructured text. Most existing methods train an
extraction model on training data involving specific target relations, and are
incapable of extracting new relations that were not observed at training time.
Generalizing the model to unseen relations typically requires fine-tuning on
synthetic training data which is often noisy and unreliable. We show that by
reducing triplet extraction to a template infilling task over a pre-trained
language model (LM), we can equip the extraction model with zero-shot learning
capabilities and eliminate the need for additional training data. We propose a
novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling),
that aligns the task objective to the pre-training objective of generative
transformers to generalize to unseen relations. Experiments on FewRel and
Wiki-ZSL datasets demonstrate that ZETT shows consistent and stable
performance, outperforming previous state-of-the-art methods, even when using
automatically generated templates. https://github.com/megagonlabs/zett/
