Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding. (arXiv:2205.03656v2 [cs.CL] UPDATED)

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 …

arxiv cross-lingual language spoken language understanding understanding