HC ^2L: Hybrid and Cooperative Contrastive Learning for Cross-Lingual Spoken Language Understanding

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 lab...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2024-12, Vol.46 (12), p.8094-8105
Hauptverfasser:	Xing, Bowen, Tsang, Ivor W.
Format:	Artikel
Sprache:	eng
Schlagworte:	contrastive learning cross-lingual Data models Decoding Dialog system Filling Labeling Semantics spoken language understanding Task analysis Training
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