Scaling Mutual Intent-Slot Guidance Effectiveness with Pre-trained Language 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 effectiveness of mutual intent-slot guidance in zero-shot cross-lingual SLU scale with the size of the pre-trained language model (e.g., small vs. large LLMs) as measured by F1 scores on MultiATIS++ and MultiSNIPS?

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