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In this paper we explore the use of symbolic knowledge and machine teachingto reduce human data labeling efforts in building neural task bots. We proposeSYNERGY, a hybrid learning framework where a task bot is developed in twosteps: (i) Symbolic knowledge to neural networks: Large amounts of simulateddialog sessions are generated based on task-specific symbolic knowledge whichis represented as a task schema consisting of dialog flows and task-orienteddatabases. Then a pre-trained neural dialog model, SOLOIST, is fine-tuned onthe simulated dialogs to build a bot for the task. (ii) Neural learning: Thefine-tuned neural dialog model is continually refined with a handful of realtask-specific dialogs via machine teaching, where training samples aregenerated by human teachers interacting with the task bot. We validate SYNERGYon four dialog tasks. Experimental results show that SYNERGY maps task-specificknowledge into neural dialog models achieving greater diversity and coverage ofdialog flows, and continually improves model performance with machine teaching,thus demonstrating strong synergistic effects of symbolic knowledge and machineteaching.

SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching