  Recently, the task of distantly supervised (DS) ultra-fine entity typing has
received significant attention. However, DS data is noisy and often suffers
from missing or wrong labeling issues resulting in low precision and low
recall. This paper proposes a novel ultra-fine entity typing model with
denoising capability. Specifically, we build a noise model to estimate the
unknown labeling noise distribution over input contexts and noisy type labels.
With the noise model, more trustworthy labels can be recovered by subtracting
the estimated noise from the input. Furthermore, we propose an entity typing
model, which adopts a bi-encoder architecture, is trained on the denoised data.
Finally, the noise model and entity typing model are trained iteratively to
enhance each other. We conduct extensive experiments on the Ultra-Fine entity
typing dataset as well as OntoNotes dataset and demonstrate that our approach
significantly outperforms other baseline methods.
