  State-of-the-art model for zero-shot cross-lingual spoken language
understanding performs cross-lingual unsupervised contrastive learning to
achieve the label-agnostic semantic alignment between each utterance and its
code-switched data. However, it ignores the precious intent/slot labels, whose
label information is promising to help capture the label-aware semantics
structure and then leverage supervised contrastive learning to improve both
source and target languages' semantics. In this paper, we propose Hybrid and
Cooperative Contrastive Learning to address this problem. Apart from
cross-lingual unsupervised contrastive learning, we design a holistic approach
that exploits source language supervised contrastive learning, cross-lingual
supervised contrastive learning and multilingual supervised contrastive
learning to perform label-aware semantics alignments in a comprehensive manner.
Each kind of supervised contrastive learning mechanism includes both
single-task and joint-task scenarios. In our model, one contrastive learning
mechanism's input is enhanced by others. Thus the total four contrastive
learning mechanisms are cooperative to learn more consistent and discriminative
representations in the virtuous cycle during the training process. Experiments
show that our model obtains consistent improvements over 9 languages, achieving
new state-of-the-art performance.
