Before revision:  In this work , we provide an extensive overview of existing efforts in multilingual ToD and analyse the factors preventing the development of truly multilingual ToD systems . We identify two main challenges that combined hinder the faster progress in multilingual ToD: (1) current state-of-the-art ToD models based on large pretrained neural language models are data hungry; at the same time (2) data acquisition for ToD use cases is expensive and tedious.
After revision:  In this work , we provide an extensive overview of existing efforts in multilingual ToD and analyse the factors preventing the development of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation. In fact, acquiring annotations or human feedback for each component of modular systems or for data-hungry end-to-end systems is expensive and tedious.