  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.
