Although spoken language understanding (SLU) has achieved great success in high-resource languages, such as English, it remains challenging in low-resource languages mainly due to the lack of high quality training data. The recent multilingual code-switching approach samples some words in an input utterance and replaces them by expressions in some other languages of the same meaning. The multilingual code-switching approach achieves better alignments of representations across languages in zero-shot cross-lingual SLU. Surprisingly, all existing multilingual code-switching methods disregard the inherent semantic structure in SLU, i.e., most utterances contain one or more slots, and each slot consists of one or more words. In this paper, we propose to exploit the "utterance-slot-word" structure of SLU and systematically model this structure by a multi-level contrastive learning framework at the utterance, slot, and word levels. We develop novel code-switching schemes to generate hard negative examples for contrastive learning at all levels. Furthermore, we develop a label-aware joint model to leverage label semantics for cross-lingual knowledge transfer. Our experimental results show that our proposed methods significantly improve the performance compared with the strong baselines on two zero-shot cross-lingual SLU benchmark datasets.

本研究提出一个基于对话、槽位和单词水平的对比学习框架来显式对齐跨语言口语理解中隐含语义结构的多语种混合方法，通过引入硬负样本和标签关联联合模型，取得了两个零-shot跨语言口语理解的基准数据集中显著的性能改进。

面向跨语言口语理解的标签感知多层对比学习