Sequence labeling systems should perform reliably not only under ideal
conditions but also with corrupted inputs - as these systems often process
user-generated text or follow an error-prone upstream component. To this end,
we formulate the noisy sequence labeling problem, where the input may undergo
an unknown noising process and propose two Noise-Aware Training (NAT)
objectives that improve robustness of sequence labeling performed on perturbed
input: Our data augmentation method trains a neural model using a mixture of
clean and noisy samples, whereas our stability training algorithm encourages
the model to create a noise-invariant latent representation. We employ a
vanilla noise model at training time. For evaluation, we use both the original
data and its variants perturbed with real OCR errors and misspellings.
Extensive experiments on English and German named entity recognition benchmarks
confirmed that NAT consistently improved robustness of popular sequence
labeling models, preserving accuracy on the original input. We make our code
and data publicly available for the research community.

本文提出了两种噪声感知训练对象来提高对受干扰输入的序列标记的鲁棒性，一种是使用混合干净和嘈杂样本的数据增强方法来训练神经模型，另外一种是通过稳定性训练算法鼓励模型生成噪声不变的潜在表示，实验结果证实，这种方法能够提高流行的序列标记模型的鲁棒性，保持原始输入的准确性。

NAT: 受噪声影响的训练用于强健神经序列标记

NAT: Noise-Aware Training for Robust Neural Sequence Labeling

In this paper we address the issue of output instability of deep neural
networks: small perturbations in the visual input can significantly distort the
feature embeddings and output of a neural network. Such instability affects
many deep architectures with state-of-the-art performance on a wide range of
computer vision tasks. We present a general stability training method to
stabilize deep networks against small input distortions that result from
various types of common image processing, such as compression, rescaling, and
cropping. We validate our method by stabilizing the state-of-the-art Inception
architecture against these types of distortions. In addition, we demonstrate
that our stabilized model gives robust state-of-the-art performance on
large-scale near-duplicate detection, similar-image ranking, and classification
on noisy datasets.

本研究解决了深度神经网络输出不稳定的问题，通过提出一种稳定训练方法，使其能够应对常见图像处理带来的输入扭曲，并在大规模近似重复检测、类图像排序和嘈杂数据集分类等方面取得了鲁棒且最先进的性能。