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Building an end-to-end conversational agent for multi-domain task-orienteddialogues has been an open challenge for two main reasons. First, trackingdialogue states of multiple domains is non-trivial as the dialogue agent mustobtain complete states from all relevant domains, some of which might haveshared slots among domains as well as unique slots specifically for one domainonly. Second, the dialogue agent must also process various types of informationacross domains, including dialogue context, dialogue states, and database, togenerate natural responses to users. Unlike the existing approaches that areoften designed to train each module separately, we propose "UniConv" -- a novelunified neural architecture for end-to-end conversational systems inmulti-domain task-oriented dialogues, which is designed to jointly train (i) aBi-level State Tracker which tracks dialogue states by learning signals at bothslot and domain level independently, and (ii) a Joint Dialogue Act and ResponseGenerator which incorporates information from various input components andmodels dialogue acts and target responses simultaneously. We conductcomprehensive experiments in dialogue state tracking, context-to-text, andend-to-end settings on the MultiWOZ2.1 benchmark, achieving superiorperformance over competitive baselines.

UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues