  Despite the fact that natural language conversations with machines represent
one of the central objectives of AI, and despite the massive increase of
research and development efforts in conversational AI, task-oriented dialogue
(ToD) -- i.e., conversations with an artificial agent with the aim of
completing a concrete task -- is currently limited to a few narrow domains
(e.g., food ordering, ticket booking) and a handful of major languages (e.g.,
English, Chinese). 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. Most existing approaches to multilingual ToD thus rely
on (zero- or few-shot) cross-lingual transfer from resource-rich languages (in
ToD, this is basically only English), either by means of (i) machine
translation or (ii) multilingual representation spaces. However, such
approaches are currently not a viable solution for a large number of
low-resource languages without parallel data and/or limited monolingual
corpora. Finally, we discuss critical challenges and potential solutions by
drawing parallels between ToD and other cross-lingual and multilingual NLP
research.
