  Ultra-fine entity typing plays a crucial role in information extraction by
predicting fine-grained semantic types for entity mentions in text. However,
this task poses significant challenges due to the massive number of entity
types in the output space. The current state-of-the-art approaches, based on
standard multi-label classifiers or cross-encoder models, suffer from poor
generalization performance or inefficient inference. In this paper, we present
CASENT, a seq2seq model designed for ultra-fine entity typing that predicts
ultra-fine types with calibrated confidence scores. Our model takes an entity
mention as input and employs constrained beam search to generate multiple types
autoregressively. The raw sequence probabilities associated with the predicted
types are then transformed into confidence scores using a novel calibration
method. We conduct extensive experiments on the UFET dataset which contains
over 10k types. Our method outperforms the previous state-of-the-art in terms
of F1 score and calibration error, while achieving an inference speedup of over
50 times. Additionally, we demonstrate the generalization capabilities of our
model by evaluating it in zero-shot and few-shot settings on five specialized
domain entity typing datasets that are unseen during training. Remarkably, our
model outperforms large language models with 10 times more parameters in the
zero-shot setting, and when fine-tuned on 50 examples, it significantly
outperforms ChatGPT on all datasets. Our code, models and demo are available at
https://github.com/yanlinf/CASENT.
