From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained (2023)

Abstract: 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.

## introduction entity typing is the task of assigning type labels to entity mentions in texts. its results have been shown to be beneficial to downstream tasks such as entity linking @xcite , coreference resolution @xcite , etc. currently, there are mainly two forms of entity typing tasks: fine-grained entity typing (fet) @xcite and ultra-fine entity typing (ufet) @xcite . ## related work for both ufet and fet, due to the use of large entity type sets, it is labor-intensive to manually annotate training examples. thus, different approaches @xcite @xcite of automatically generating weakly labeled training examples are proposed. among them, the most commonly used method is to link entity mentions to a knowledge base, and then use the types of the corresponding entities as labels @xcite @xcite . additionally, @xcite propose to use the head word of the mention phrase as its type label. @xcite generate entity type labels for mentions with a prompt-based method. with different ways to create large amounts of training data automatically, the incorrectness of the generated labels become a problem. many entity typing studies @xcite @xcite seek to obtain better models when using weak training data. for example, @xcite durrett (2019 recently, there are more entity typing studies that do not follow the commonly adopted approach of training with distantly labeled data created by using a knowledge base. some of them also do not require a designated training set for each entity type schema. for example, @xcite al. (2022) ## experiments we conduct experiments on both ufet and fet datasets. in this section, we use fivefine to denote our approach (because there are five "fines" in the title of this paper). ## conclusion in this paper, we propose the approach to fine-tune a ufet model to fet models, which can avoid the requirement of constructing distantly labeled training data when an application needs to train a model for a newly designed fet type schema. this approach is feasible because the type schema used by ufet have very broad type coverage, usually much broader than fet tasks. we also propose an entity typing model that treats target entity type labels as words/phrases. this allows all the trained parameters of the model to be reused when finetuned from ufet to fet, so that the trained ufet model can be better exploited. the experiments we conduct verify the effectiveness of both our ufet model, and the fet models that are fine-tuned from it with small sized training sets.