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.

本文提出一种新颖的超细实体类型模型，其具有去噪能力，通过建立一个噪声模型对输入的上下文和实体类型标签进行估计，从输入中减去估计噪声以恢复更可靠的标签，并采用双编码器结构训练去噪数据，噪声模型和实体类型模型迭代训练，以提高彼此的性能，在 Ultra-Fine 实体类型数据集以及 OntoNotes 数据集上表现显著优于其他基线方法。