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Recently, pre-training methods have shown remarkable success in task-orienteddialog (TOD) systems. However, most existing pre-trained models for TOD focuson either dialog understanding or dialog generation, but not both. In thispaper, we propose SPACE-3, a novel unified semi-supervised pre-trainedconversation model learning from large-scale dialog corpora with limitedannotations, which can be effectively fine-tuned on a wide range of downstreamdialog tasks. Specifically, SPACE-3 consists of four successive components in asingle transformer to maintain a task-flow in TOD systems: (i) a dialogencoding module to encode dialog history, (ii) a dialog understanding module toextract semantic vectors from either user queries or system responses, (iii) adialog policy module to generate a policy vector that contains high-levelsemantics of the response, and (iv) a dialog generation module to produceappropriate responses. We design a dedicated pre-training objective for eachcomponent. Concretely, we pre-train the dialog encoding module with span masklanguage modeling to learn contextualized dialog information. To capture thestructured dialog semantics, we pre-train the dialog understanding module via anovel tree-induced semi-supervised contrastive learning objective with the helpof extra dialog annotations. In addition, we pre-train the dialog policy moduleby minimizing the L2 distance between its output policy vector and the semanticvector of the response for policy optimization. Finally, the dialog generationmodel is pre-trained by language modeling. Results show that SPACE-3 achievesstate-of-the-art performance on eight downstream dialog benchmarks, includingintent prediction, dialog state tracking, and end-to-end dialog modeling. Wealso show that SPACE-3 has a stronger few-shot ability than existing modelsunder the low-resource setting.

SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation