For the task of fine-grained entity typing (FET), due to the use of a large number of entity types, it is usually considered too costly to manually annotating a training dataset that contains an ample number of examples for each type. A common way to address this problem is to use distantly annotated training data that contains incorrect labels. However, the performance of models trained solely with such data can be limited by the errors in the automatic annotation. Recently, there are a few approaches that no longer follow this conventional way. But without using sufficient direct entity typing supervision may also cause them to yield inferior performance. In this paper, we propose a new approach that can avoid the need of creating distantly labeled data whenever there is a new type schema. We first train an entity typing model that have an extremely board type coverage by using the ultra-fine entity typing data. Then, when there is a need to produce a model for a newly designed fine-grained entity type schema. We can simply fine-tune the previously trained model with a small number of examples annotated under this schema. Experimental results show that our approach achieves outstanding performance for FET under the few-shot setting. It can also outperform state-of-the-art weak supervision based methods after fine-tuning the model with only a small size manually annotated training set.

翻译：针对细粒度实体类型分类（FET）任务，由于使用大量实体类型，通常认为为每个类型手动标注包含充足样本的训练数据集成本过高。解决该问题的常见方法是使用包含错误标签的远程标注训练数据。然而，仅以此类数据训练的模型性能可能受限于自动标注中的错误。近年来，部分方法不再遵循这一传统路径，但在缺乏充分的直接实体类型监督信号时，其性能也可能不佳。本文提出一种新方法，可在新类型体系出现时避免生成远程标注数据。我们首先利用超细粒度实体类型数据训练一个类型覆盖范围极广的实体类型模型。随后，当需要为新型细粒度实体类型体系构建模型时，仅需使用该体系下少量标注样本对预训练模型进行微调。实验结果表明，该方法在少样本场景下实现卓越的FET性能，且在使用少量人工标注训练集微调模型后，其表现可超越基于弱监督的最先进方法。