From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to
  Fine-grained
Hongliang Dai Ziqian Zeng
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.