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Most existing slot filling models tend to memorize inherent patterns ofentities and corresponding contexts from training data. However, these modelscan lead to system failure or undesirable outputs when being exposed to spokenlanguage perturbation or variation in practice. We propose a perturbed semanticstructure awareness transferring method for training perturbation-robust slotfilling models. Specifically, we introduce two MLM-based training strategies torespectively learn contextual semantic structure and word distribution fromunsupervised language perturbation corpus. Then, we transfer semantic knowledgelearned from upstream training procedure into the original samples and filtergenerated data by consistency processing. These procedures aim to enhance therobustness of slot filling models. Experimental results show that our methodconsistently outperforms the previous basic methods and gains stronggeneralization while preventing the model from memorizing inherent patterns ofentities and contexts.

PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling