Adversarial Training for Robust Zero-Shot Cross-Lingual SLU with Mutual Intent-Slot Guidance

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: What is the impact of adversarial training on the robustness of zero-shot cross-lingual SLU models with mutual intent-slot guidance, as evaluated on the MultiSNIPS dataset under domain shift conditions, measured by slot-filling precision and intent detection recall?

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