Triplet extraction aims to extract entities and their corresponding relations
in unstructured text. Most existing methods train an extraction model on
high-quality training data, and hence are incapable of extracting relations
that were not observed during training. Generalizing the model to unseen
relations typically requires fine-tuning on synthetic training data which is
often noisy and unreliable. In this paper, we argue that reducing triplet
extraction to a template filling task over a pre-trained language model can
equip the model with zero-shot learning capabilities and enable it to leverage
the implicit knowledge in the language model. Embodying these ideas, we propose
a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling),
that is based on end-to-end generative transformers. Our experiments show that
without any data augmentation or pipeline systems, ZETT can outperform previous
state-of-the-art models with 25% less parameters. We further show that ZETT is
more robust in detecting entities and can be incorporated with automatically
generated templates for relations.

本文提出一种新的框架 ZETT，基于预训练语言模型，并采用端到端生成式 Transformer 技术，将三元组抽取任务简化为模板填充任务从而具备了零 - shot 学习能力，并能利用语言模型中的隐含知识，实验表明该方法可以在不进行数据增强和流水线系统的情况下超过之前的最优模型，对实体的检测也更为稳健。