From Cambridge to Pisa: A Journey into Cross-Lingual Dialogue Domain Adaptation for Conversational Agents

Abstract

The widespread use of conversational and question answering systems made it necessary improve the performances of speaker intent detection and understanding of related semantic slots, i.e., Spoken Language Understanding (SLU). Often, these tasks are approached with supervised learning methods, which needs considerable labeled datasets. This paper 1 presents the first Italian dataset for SLU in voice assistants scenario. It is the product of a semi-automatic procedure and is used as a benchmark of various open source and commercial systems.