  Building an end-to-end conversational agent for multi-domain task-oriented
dialogues has been an open challenge for two main reasons. First, tracking
dialogue states of multiple domains is non-trivial as the dialogue agent must
obtain complete states from all relevant domains, some of which might have
shared slots among domains as well as unique slots specifically for one domain
only. Second, the dialogue agent must also process various types of information
across domains, including dialogue context, dialogue states, and database, to
generate natural responses to users. Unlike the existing approaches that are
often designed to train each module separately, we propose "UniConv" -- a novel
unified neural architecture for end-to-end conversational systems in
multi-domain task-oriented dialogues, which is designed to jointly train (i) a
Bi-level State Tracker which tracks dialogue states by learning signals at both
slot and domain level independently, and (ii) a Joint Dialogue Act and Response
Generator which incorporates information from various input components and
models dialogue acts and target responses simultaneously. We conduct
comprehensive experiments in dialogue state tracking, context-to-text, and
end-to-end settings on the MultiWOZ2.1 benchmark, achieving superior
performance over competitive baselines.
