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.

通过使用超细粒度实体类型数据训练一个具有极广泛类型覆盖的实体类型模型，然后在新设计的细粒度实体类型模式下，通过少量手动标注的样本对先前训练的模型进行微调，我们提出了一种新的方法，能够避免在新类型架构时创建远距离标记数据的需求，并在低样本学习设置下实现出色的细粒度实体类型识别性能。

从超细到精细：微调超细实体类型模型至细粒度

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

Neural entity linking models are very powerful, but run the risk of
overfitting to the domain they are trained in. For this problem, a domain is
characterized not just by genre of text but even by factors as specific as the
particular distribution of entities, as neural models tend to overfit by
memorizing properties of frequent entities in a dataset. We tackle the problem
of building robust entity linking models that generalize effectively and do not
rely on labeled entity linking data with a specific entity distribution. Rather
than predicting entities directly, our approach models fine-grained entity
properties, which can help disambiguate between even closely related entities.
We derive a large inventory of types (tens of thousands) from Wikipedia
categories, and use hyperlinked mentions in Wikipedia to distantly label data
and train an entity typing model. At test time, we classify a mention with this
typing model and use soft type predictions to link the mention to the most
similar candidate entity. We evaluate our entity linking system on the
CoNLL-YAGO dataset (Hoffart et al., 2011) and show that our approach
outperforms prior domain-independent entity linking systems. We also test our
approach in a harder setting derived from the WikilinksNED dataset (Eshel et
al., 2017) where all the mention-entity pairs are unseen during test time.
Results indicate that our approach generalizes better than a state-of-the-art
neural model on the dataset.

该论文介绍了一种可以构建鲁棒实体链接模型的方法，该方法将注意力放在建立细粒度实体属性的模型上，具有更好的泛化性能，使用维基百科类别的大型库存来远程标记数据并训练实体类型模型，使用软类型预测将提及链接到最相似的候选实体上。