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For the task of fine-grained entity typing (FET), due to the use of a largenumber of entity types, it is usually considered too costly to manuallyannotating a training dataset that contains an ample number of examples foreach type. A common way to address this problem is to use distantly annotatedtraining data that contains incorrect labels. However, the performance ofmodels trained solely with such data can be limited by the errors in theautomatic annotation. Recently, there are a few approaches that no longerfollow this conventional way. But without using sufficient direct entity typingsupervision may also cause them to yield inferior performance. In this paper,we propose a new approach that can avoid the need of creating distantly labeleddata whenever there is a new type schema. We first train an entity typing modelthat have an extremely board type coverage by using the ultra-fine entitytyping data. Then, when there is a need to produce a model for a newly designedfine-grained entity type schema. We can simply fine-tune the previously trainedmodel with a small number of examples annotated under this schema. Experimentalresults show that our approach achieves outstanding performance for FET underthe few-shot setting. It can also outperform state-of-the-art weak supervisionbased methods after fine-tuning the model with only a small size manuallyannotated training set.

From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained