Zero-Shot Adaptive Transfer for Conversational Language Understanding

Understand

Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains.

• A massive amount of labeled data is required for training each new domain.
• While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments.
• To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments.