Performance Scaling of Intent-Slot Mutual Guidance Frameworks with Model Size in Zero-Shot Cross-Lingual SLU

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 performance of intent-slot mutual guidance frameworks scale with model size (e.g., mT5-Base vs. mT5-XL) on zero-shot cross-lingual SLU tasks, as measured by F1 scores on MultiATIS++ and MultiSNIPS benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.