Scalability of Mutual Intent-Slot Guidance in Zero-Shot SLU Across Typologically Diverse 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 performance of mutual intent-slot guidance mechanisms in zero-shot SLU scale when transferring from high-resource languages to typologically distant low-resource languages in the MultiATIS++ dataset?

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