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。