Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems

Understand

Recently, data-driven task-oriented dialogue systems have achieved promising performance in English.

• However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data.
• In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems.
• It leverages very few task-related parallel word pairs to generate code-switching sentences for learning the inter-lingual semantics across languages.