Performance Comparison of IKD-SLU with State-of-the-Art Zero-Shot Cross-Lingual SLU Models on Low-Resource Languages

Spoken language understanding (SLU) typically includes two subtasks: intent detection and slot filling. Currently, it has achieved great success in high-resource languages, but it still remains challenging in low-resource languages due to the scarcity of labeled training data. Hence, there is a growing interest in zero-shot cross-lingual SLU. Despite of the success of existing zero-shot cross-lingual SLU models, most of them neglect to achieve the mutual guidance between intent and slots. To address this issue, we propose an Intra-Inter Knowledge Distillation framework for zero-shot cross-ling

Research goal: How does the I²KD-SLU framework compare to other state-of-the-art zero-shot cross-lingual SLU models (e.g., XLM-R, mBERT) in terms of accuracy degradation when scaling to 10+ low-resource languages using the MultiSNIPS dataset?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.