  In real dialogue scenarios, the existing slot filling model, which tends to
memorize entity patterns, has a significantly reduced generalization facing
Out-of-Vocabulary (OOV) problems. To address this issue, we propose an OOV
robust slot filling model based on multi-level data augmentations to solve the
OOV problem from both word and slot perspectives. We present a unified
contrastive learning framework, which pull representations of the origin sample
and augmentation samples together, to make the model resistant to OOV problems.
We evaluate the performance of the model from some specific slots and carefully
design test data with OOV word perturbation to further demonstrate the
effectiveness of OOV words. Experiments on two datasets show that our approach
outperforms the previous sota methods in terms of both OOV slots and words.
