Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates
the data scarcity problem in NER by automatically generating training samples.
Unfortunately, the distant supervision may induce noisy labels, thus
undermining the robustness of the learned models and restricting the practical
application. To relieve this problem, recent works adopt self-training
teacher-student frameworks to gradually refine the training labels and improve
the generalization ability of NER models. However, we argue that the
performance of the current self-training frameworks for DS-NER is severely
underestimated by their plain designs, including both inadequate student
learning and coarse-grained teacher updating. Therefore, in this paper, we make
the first attempt to alleviate these issues by proposing: (1) adaptive teacher
learning comprised of joint training of two teacher-student networks and
considering both consistent and inconsistent predictions between two teachers,
thus promoting comprehensive student learning. (2) fine-grained student
ensemble that updates each fragment of the teacher model with a temporal moving
average of the corresponding fragment of the student, which enhances consistent
predictions on each model fragment against noise. To verify the effectiveness
of our proposed method, we conduct experiments on four DS-NER datasets. The
experimental results demonstrate that our method significantly surpasses
previous SOTA methods.

本文提出了一种适应性教师学习和细粒度学生集成的神经网络模型，用于解决 Named Entity Recognition 中远程监督训练数据不足和训练标签不准确的问题，实验证明该方法显著优于现有的最佳方法。

使用自适应教师学习和细粒度学生集成的远程监督命名实体识别

Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble

A quality abstractive summary should not only copy salient source texts as
summaries but should also tend to generate new conceptual words to express
concrete details. Inspired by the popular pointer generator
sequence-to-sequence model, this paper presents a concept pointer network for
improving these aspects of abstractive summarization. The network leverages
knowledge-based, context-aware conceptualizations to derive an extended set of
candidate concepts. The model then points to the most appropriate choice using
both the concept set and original source text. This joint approach generates
abstractive summaries with higher-level semantic concepts. The training model
is also optimized in a way that adapts to different data, which is based on a
novel method of distantly-supervised learning guided by reference summaries and
testing set. Overall, the proposed approach provides statistically significant
improvements over several state-of-the-art models on both the DUC-2004 and
Gigaword datasets. A human evaluation of the model's abstractive abilities also
supports the quality of the summaries produced within this framework.

本论文提出基于概念指针网络的抽象总结方法，使得生成的总结不仅保留了原文的关键信息，同时还生成了新的概念词汇来表达具体细节，而且采用了一种新颖的远程监督学习方法来优化训练模型。实验结果表明，该方法在 DUC-2004 和 Gigaword 数据集上显著优于多种最先进的模型。