  Spoken language understanding (SLU) system usually consists of various
pipeline components, where each component heavily relies on the results of its
upstream ones. For example, Intent detection (ID), and slot filling (SF)
require its upstream automatic speech recognition (ASR) to transform the voice
into text. In this case, the upstream perturbations, e.g. ASR errors,
environmental noise and careless user speaking, will propagate to the ID and SF
models, thus deteriorating the system performance. Therefore, the
well-performing SF and ID models are expected to be noise resistant to some
extent. However, existing models are trained on clean data, which causes a
\textit{gap between clean data training and real-world inference.} To bridge
the gap, we propose a method from the perspective of domain adaptation, by
which both high- and low-quality samples are embedding into similar vector
space. Meanwhile, we design a denoising generation model to reduce the impact
of the low-quality samples. Experiments on the widely-used dataset, i.e. Snips,
and large scale in-house dataset (10 million training examples) demonstrate
that this method not only outperforms the baseline models on real-world (noisy)
corpus but also enhances the robustness, that is, it produces high-quality
results under a noisy environment. The source code will be released.
