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 .
In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent to complete a concrete task. Although this technology represents one of the central objectives of AI and has been the focus of ever more intense research and development efforts , it is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of languages (e.g., English, Chinese). This work provides an extensive overview of existing methods and resources in multilingual ToD as an entry point to this exciting and emerging field. We find that the most critical factor preventing the creation 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. Hence, state-of-the-art approaches to multilingual ToD mostly rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages ( almost exclusively English), either by means of machine translation or multilingual representations. These approaches are currently viable only for typologically similar languages and languages with parallel / monolingual corpora available. On the other hand, their effectiveness beyond these boundaries is doubtful or hard to assess due to the lack of linguistically diverse benchmarks (especially for natural language generation and end-to-end evaluation). To overcome this limitation, we draw parallels between components of the ToD pipeline and other NLP tasks, which can inspire solutions for learning in low-resource scenarios. Finally, we list additional challenges that multilinguality poses for related areas (such as speech and human-centred evaluation), and indicate future directions that hold promise to further expand language coverage and dialogue capabilities of current ToD systems .
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