DiffusionNER: Boundary Diffusion for Named Entity Recognition
Yongliang Shen Kaitao Song Xu Tan Dongsheng Li Weiming Lu Yueting Zhuang
In this paper, we propose DiffusionNER, which formulates the named entity
recognition task as a boundary-denoising diffusion process and thus generates
named entities from noisy spans. During training, DiffusionNER gradually adds
noises to the golden entity boundaries by a fixed forward diffusion process and
learns a reverse diffusion process to recover the entity boundaries. In
inference, DiffusionNER first randomly samples some noisy spans from a standard
Gaussian distribution and then generates the named entities by denoising them
with the learned reverse diffusion process. The proposed boundary-denoising
diffusion process allows progressive refinement and dynamic sampling of
entities, empowering DiffusionNER with efficient and flexible entity generation
capability. Experiments on multiple flat and nested NER datasets demonstrate
that DiffusionNER achieves comparable or even better performance than previous
state-of-the-art models.