Towards Robust and Generalizable Training: An Empirical Study of Noisy
  Slot Filling for Input Perturbations
Jiachi Liu Liwen Wang Guanting Dong Xiaoshuai Song Zechen Wang Zhengyang Wang Shanglin Lei Jinzheng Zhao Keqing He Bo Xiao Weiran Xu
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
https://github.com/dongguanting/Noise-SF.