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:零热关系提取( ZRE) 是Relation Expliton 的任务, 培训和测试组没有共享关系类型。 这个非常具有挑战性的领域是模型普及能力的良好测试。 先前的 ZRE 重新框架关系提取方法是问答( QA), 允许使用经过预先训练的 QA 模型。 但是, 这个方法需要手工为每个新关系创建金质问题模板。 这里, 我们用这些金质模板来做, 而不是学习一个能为无形关系产生问题的模型。 我们的技术可以成功地将关系描述转换为相关问题, 然后被利用来生成正确的尾巴实体。 在尾巴提取时, 我们不用金质问题模板, 将先前的F1点比原状态提取( QA) 。 在RE 问题模板中, 我们的拟议算法在使用金质模板的系统 0. 7 F1 点之内。 我们的模型也超越了在 少Rel 和 WikizSL Q 数据模板上的最新 Z- 基准, 显示我们任务模板 。