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.

零射击跨语言口语理解的最新模型进行了跨语言无监督对比学习，以实现每个话语与其代码切换数据之间的标签不可知的语义对齐。然而，它忽视了宝贵的意图 / 槽标签，其标签信息有望帮助捕捉标签感知的语义结构，然后利用监督对比学习来改进源语言和目标语言的语义。在本文中，我们提出了混合和合作对比学习来解决这个问题。除了跨语言无监督对比学习外，我们设计了一个全面的方法，能够综合地利用源语言的监督对比学习、跨语言的监督对比学习和多语言的监督对比学习来进行标签感知的语义对齐。每种监督对比学习机制都包括单任务和联合任务场景。在我们的模型中，一个对比学习机制的输入通过其他机制得到增强。因此，在训练过程中，这四种对比学习机制共同学习，形成一个良性循环，得到更一致和有区别性的表示。实验证明我们的模型在 9 种语言上获得了一致的改善，达到了最新的最佳性能。