Despite the great success of spoken language understanding (SLU) in
high-resource languages, it remains challenging in low-resource languages
mainly due to the lack of labeled training data. The recent multilingual
code-switching approach achieves better alignments of model representations
across languages by constructing a mixed-language context in zero-shot
cross-lingual SLU. However, current code-switching methods are limited to
implicit alignment and disregard the inherent semantic structure in SLU, i.e.,
the hierarchical inclusion of utterances, slots, and words. In this paper, we
propose to model the utterance-slot-word structure by a multi-level contrastive
learning framework at the utterance, slot, and word levels to facilitate
explicit alignment. Novel code-switching schemes are introduced to generate
hard negative examples for our contrastive learning framework. Furthermore, we
develop a label-aware joint model leveraging label semantics to enhance the
implicit alignment and feed to contrastive learning. 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。