In real dialogue scenarios, as there are unknown input noises in the
utterances, existing supervised slot filling models often perform poorly in
practical applications. Even though there are some studies on noise-robust
models, these works are only evaluated on rule-based synthetic datasets, which
is limiting, making it difficult to promote the research of noise-robust
methods. In this paper, we introduce a noise robustness evaluation dataset
named Noise-SF for slot filling task. The proposed dataset contains five types
of human-annotated noise, and all those noises are exactly existed in real
extensive robust-training methods of slot filling into the proposed framework.
By conducting exhaustive empirical evaluation experiments on Noise-SF, we find
that baseline models have poor performance in robustness evaluation, and the
proposed framework can effectively improve the robustness of models. Based on
the empirical experimental results, we make some forward-looking suggestions to
fuel the research in this direction. Our dataset Noise-SF will be released at
this https URL

存在对话场景中的未知输入噪声，已有的受监督槽填充模型在实际应用中性能低下；通过引入一个名为 Noise-SF 的噪声鲁棒性评估数据集，本文的提出的框架可以有效地提高模型的鲁棒性。

朝向稳健且通用的训练：基于输入扰动的噪声槽填充的实证研究

Towards Robust and Generalizable Training: An Empirical Study of Noisy  Slot Filling for Input Perturbations

Constructing fine-grained image datasets typically requires domain-specific
expert knowledge, which is not always available for crowd-sourcing platform
annotators. Accordingly, learning directly from web images becomes an
alternative method for fine-grained visual recognition. However, label noise in
the web training set can severely degrade the model performance. To this end,
we propose a data-driven meta-set based approach to deal with noisy web images
for fine-grained recognition. Specifically, guided by a small amount of clean
meta-set, we train a selection net in a meta-learning manner to distinguish in-
and out-of-distribution noisy images. To further boost the robustness of model,
we also learn a labeling net to correct the labels of in-distribution noisy
data. In this way, our proposed method can alleviate the harmful effects caused
by out-of-distribution noise and properly exploit the in-distribution noisy
samples for training. Extensive experiments on three commonly used fine-grained
datasets demonstrate that our approach is much superior to state-of-the-art
noise-robust methods.

通过学习元数据集并应用数据驱动的元集合方法，我们可以解决使用网络图像进行细粒度图像识别时带有标签噪声的问题，并且相比于现有噪声鲁棒性方法，我们的方法要优越得多。