Zero-Shot Relation Extraction (ZRE) is the task of Relation Extraction where the training and test sets have no shared relation types. This very challenging domain is a good test of a model's ability to generalize. Previous approaches to ZRE reframed relation extraction as Question Answering (QA), allowing for the use of pre-trained QA models. However, this method required manually creating gold question templates for each new relation. Here, we do away with these gold templates and instead learn a model that can generate questions for unseen relations. Our technique can successfully translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. On tail entity extraction, we outperform the previous state-of-the-art by more than 16 F1 points without using gold question templates. On the RE-QA dataset where no previous baseline for relation extraction exists, our proposed algorithm comes within 0.7 F1 points of a system that uses gold question templates. Our model also outperforms the state-of-the-art ZRE baselines on the FewRel and WikiZSL datasets, showing that QA models no longer need template questions to match the performance of models specifically tailored to the ZRE task. Our implementation is available at https://github.com/fyshelab/QA-ZRE.

翻译：零样本关系抽取（Zero-Shot Relation Extraction, ZRE）是一项训练集与测试集不存在共享关系类型的关系抽取任务。这一极具挑战性的领域是检验模型泛化能力的重要基准。现有ZRE方法将关系抽取重构为问答任务，从而可利用预训练问答模型。然而，该方法需要为每个新关系人工创建标准问题模板。本文摒弃标准模板，转而训练一个能针对未见关系自动生成问题的模型。我们的技术可成功将关系描述转化为相关问句，并利用这些问句生成正确的尾实体。在尾实体抽取任务中，我们在不使用标准问题模板的情况下，F1值较先前最优方法提升超过16个百分点。在尚无关系抽取基准的RE-QA数据集上，本算法与使用标准问题模板的系统仅相差0.7个F1点。在FewRel和WikiZSL数据集上，我们的模型同样超越了现有ZRE基线方法，表明问答模型无需模板问题即可达到特化ZRE模型的性能水平。相关实现代码已开源至https://github.com/fyshelab/QA-ZRE。