  Entity typing aims to assign types to the entity mentions in given texts. The
traditional classification-based entity typing paradigm has two unignorable
drawbacks: 1) it fails to assign an entity to the types beyond the predefined
type set, and 2) it can hardly handle few-shot and zero-shot situations where
many long-tail types only have few or even no training instances. To overcome
these drawbacks, we propose a novel generative entity typing (GET) paradigm:
given a text with an entity mention, the multiple types for the role that the
entity plays in the text are generated with a pre-trained language model (PLM).
However, PLMs tend to generate coarse-grained types after fine-tuning upon the
entity typing dataset. Besides, we only have heterogeneous training data
consisting of a small portion of human-annotated data and a large portion of
auto-generated but low-quality data. To tackle these problems, we employ
curriculum learning (CL) to train our GET model upon the heterogeneous data,
where the curriculum could be self-adjusted with the self-paced learning
according to its comprehension of the type granularity and data heterogeneity.
Our extensive experiments upon the datasets of different languages and
downstream tasks justify the superiority of our GET model over the
state-of-the-art entity typing models. The code has been released on
https://github.com/siyuyuan/GET.
