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Spoken language understanding (SLU) typically includes two subtasks: intentdetection and slot filling. Currently, it has achieved great success inhigh-resource languages, but it still remains challenging in low-resourcelanguages due to the scarcity of labeled training data. Hence, there is agrowing interest in zero-shot cross-lingual SLU. Despite of the success ofexisting zero-shot cross-lingual SLU models, most of them neglect to achievethe mutual guidance between intent and slots. To address this issue, we proposean Intra-Inter Knowledge Distillation framework for zero-shot cross-lingualSpoken Language Understanding (I$^2$KD-SLU) to model the mutual guidance.Specifically, we not only apply intra-knowledge distillation between intentpredictions or slot predictions of the same utterance in different languages,but also apply inter-knowledge distillation between intent predictions and slotpredictions of the same utterance. Our experimental results demonstrate thatour proposed framework significantly improves the performance compared with thestrong baselines and achieves the new state-of-the-art performance on theMultiATIS++ dataset, obtaining a significant improvement over the previous bestmodel in overall accuracy.

I$^2$KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understanding