  Distantly supervised named entity recognition (DS-NER) efficiently reduces
labor costs but meanwhile intrinsically suffers from the label noise due to the
strong assumption of distant supervision. Typically, the wrongly labeled
instances comprise numbers of incomplete and inaccurate annotation noise, while
most prior denoising works are only concerned with one kind of noise and fail
to fully explore useful information in the whole training set. To address this
issue, we propose a robust learning paradigm named Self-Collaborative Denoising
Learning (SCDL), which jointly trains two teacher-student networks in a
mutually-beneficial manner to iteratively perform noisy label refinery. Each
network is designed to exploit reliable labels via self denoising, and two
networks communicate with each other to explore unreliable annotations by
collaborative denoising. Extensive experimental results on five real-world
datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising
methods.
