  Recently deep learning has dominated many machine learning areas, including
spoken language understanding (SLU). However, deep learning models are
notorious for being data-hungry, and the heavily optimized models are usually
sensitive to the quality of the training examples provided and the consistency
between training and inference conditions. To improve the performance of SLU
models on tasks with noisy and low training resources, we propose a new SLU
benchmarking task: few-shot robust SLU, where SLU comprises two core problems,
intent classification (IC) and slot labeling (SL). We establish the task by
defining few-shot splits on three public IC/SL datasets, ATIS, SNIPS, and TOP,
and adding two types of natural noises (adaptation example missing/replacing
and modality mismatch) to the splits. We further propose a novel noise-robust
few-shot SLU model based on prototypical networks. We show the model
consistently outperforms the conventional fine-tuning baseline and another
popular meta-learning method, Model-Agnostic Meta-Learning (MAML), in terms of
achieving better IC accuracy and SL F1, and yielding smaller performance
variation when noises are present.
