We study the problem of few-shot Fine-grained Entity Typing (FET), where only
a few annotated entity mentions with contexts are given for each entity type.
Recently, prompt-based tuning has demonstrated superior performance to standard
fine-tuning in few-shot scenarios by formulating the entity type classification
task as a ''fill-in-the-blank'' problem. This allows effective utilization of
the strong language modeling capability of Pre-trained Language Models (PLMs).
Despite the success of current prompt-based tuning approaches, two major
challenges remain: (1) the verbalizer in prompts is either manually designed or
constructed from external knowledge bases, without considering the target
corpus and label hierarchy information, and (2) current approaches mainly
utilize the representation power of PLMs, but have not explored their
generation power acquired through extensive general-domain pre-training. In
this work, we propose a novel framework for few-shot FET consisting of two
modules: (1) an entity type label interpretation module automatically learns to
relate type labels to the vocabulary by jointly leveraging few-shot instances
and the label hierarchy, and (2) a type-based contextualized instance generator
produces new instances based on given instances to enlarge the training set for
better generalization. On three benchmark datasets, our model outperforms
existing methods by significant margins. Code can be found at
this https URL.

本文提出了一个新的架构，包括两个模块：（1）实体类型标签解释模块，自动学习将类型标签与词汇表相关联的方法，（2）基于类型的上下文实例生成器，根据给定实例生成新实例，以扩大训练集以获得更好的泛化能力。在三个基准数据集上，该模型的性能明显高于现有方法。