Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models

Understand

With the recent explosion in popularity of voice assistant devices, there is a growing interest in making them available to user populations in additional countries and languages.

• However, to provide the highest accuracy and best performance for specific user populations, most existing voice assistant models are developed individually for each region or language, which requires linear investment of effort.
• In this paper, we propose a general multilingual model framework for Natural Language Understanding (NLU) models, which can help bootstrap new language models faster and reduce the amount of effort required to develop each language separately.
• We explore how different deep learning architectures affect multilingual NLU model performance.