In this paper we explore the use of symbolic knowledge and machine teaching to reduce human data labeling efforts in building neural task bots. We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases. Then a pre-trained neural dialog model, SOLOIST, is fine-tuned on the simulated dialogs to build a bot for the task. (ii) Neural learning: The fine-tuned neural dialog model is continually refined with a handful of real task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the task bot. We validate SYNERGY on four dialog tasks. Experimental results show that SYNERGY maps task-specific knowledge into neural dialog models achieving greater diversity and coverage of dialog flows, and continually improves model performance with machine teaching, thus demonstrating strong synergistic effects of symbolic knowledge and machine teaching.
翻译:在本文中,我们探索利用象征性知识和机器教学来减少在建造神经任务机器人方面的人类数据标签工作。我们建议SYNERGY,这是一个混合学习框架,任务机器人分两步发展:(一) 神经网络的象征知识:大量模拟对话会是根据特定任务象征性知识产生的,这种模拟对话会是一种任务性知识,由对话流和任务导向数据库组成。然后,一个预先训练的神经对话模式SOLOIST, 正在对模拟对话模式进行微调,以便为任务构建一个机器人。 (二) 神经学习:微调的神经对话模式通过机器教学不断完善,通过少数实际任务性对话会得到精细化,其中培训样本是由与任务机器人互动的人类教师生成的。我们验证SYNERGY的四个对话任务。实验结果表明,SYNERGY将特定任务知识绘制成神经对话模式,实现更大的多样性和对话流量的覆盖面,并不断改进模型的性能,从而展示象征性知识和机器教学的强大协同效应。