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.

翻译：在真实对话场景中，现有的槽填充模型倾向于记忆实体模式，在面对词汇外（OOV）问题时泛化能力显著降低。为解决这一问题，我们提出了一种基于多层次数据增强的OOV鲁棒槽填充模型，从单词和槽两个层面解决OOV问题。我们提出了一种统一的对比学习框架，通过拉近原始样本与增强样本的表示，使模型能够抵御OOV问题。我们从特定槽的角度评估模型性能，并精心设计带有OOV单词扰动的测试数据，以进一步证明OOV单词的有效性。在两个数据集上的实验表明，我们的方法在OOV槽和单词方面均优于先前的最优方法。