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.

本文提出一种名为 “自我协同去噪学习” 的鲁棒学习模式，利用两个教师 - 学生网络相互学习进行标签去噪，以应对远程监督命名实体识别中的标签噪声问题。该方法在五个真实世界数据集上的实验结果表明其优于当前 DS-NER 去噪方法。