From Cambridge to Pisa: A Journey into Cross-Lingual Dialogue Domain Adaptation for Conversational Agents
Tiziano Labruna, Bernardo Magnini · Accademia University Press eBooks · 2022
Domain and language shift are still major bottlenecks for a vast range of task-oriented dialogue systems. This paper focuses on data-driven models for dialogue state tracking, and builds on top of recent work on dialogue domain adaptation, showing that state-of-the-art models are very sensible to language shift obtained through automatic translation. Experiments show that combining training data for the two languages (English and Italian) is always beneficial, while combining domains does not increase performance. As a relevant side effect of our work, we present a new dataset for dialogue state tracking available for Italian, derived from MultiWOZ 2.3.