The MaDrIgAL project: Multi-Dimensional Interaction Management and Adaptive Learning

Recent statistical approaches have improved the robustness and scalability of spoken dialogue systems. However, they still lack in two main aspects: 1) their perceived naturalness and social intelligence, and 2) their cross-domain scalability. In this paper, we argue that both of these shortcomings can be addressed effectively by extending current models to reflect and exploit the multi-dimensional nature of human dialogue. In order to investigate this, the MaDrIgAL project aims to develop multi-dimensional versions of data-driven models for spoken dialogue systems. In doing so, we 1) incorporate a richer set of dialogue acts into the learning process, leading to more natural and socially appropriate dialogues, and 2) learn transferable skills by separating out domain-independent dimensions of communication, leading to more efficient cross-domain adaptation.