Performance of Zero-Shot Cross-Lingual SLU Models with Mutual Intent-Slot Guidance on CrossNLU Benchmark

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 zero-shot cross-lingual SLU models with mutual intent-slot guidance compare to models using separate intent and slot predictions (measured by joint-goal accuracy) on the CrossNLU benchmark across high- and low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.