AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All
This addresses the problem of limited NLP resources for developing countries' citizens by providing a dataset to build multilingual dialog systems.
The paper tackles the language barrier in task-oriented dialog systems by introducing AllWOZ, a multilingual multi-domain dataset covering eight languages, and establishes a benchmark using mT5 with meta-learning.
A commonly observed problem of the state-of-the-art natural language technologies, such as Amazon Alexa and Apple Siri, is that their services do not extend to most developing countries' citizens due to language barriers. Such populations suffer due to the lack of available resources in their languages to build NLP products. This paper presents AllWOZ, a multilingual multi-domain task-oriented customer service dialog dataset covering eight languages: English, Mandarin, Korean, Vietnamese, Hindi, French, Portuguese, and Thai. Furthermore, we create a benchmark for our multilingual dataset by applying mT5 with meta-learning.