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.

本文介绍了一种基于多层数据扩增的 OOV 坚韧性槽填充模型，通过从词和槽的角度解决 OOV 问题。实验表明，该模型比现有模型和之前的最佳模型在 OOV 词和槽方面表现更出色。