S oloist : BuildingTask Bots at Scale with Transfer Learning and Machine Teaching

• Published in: Transactions of the Association for Computational Linguistics Vol. 9; pp. 807 - 824
• Main Authors: Peng, Baolin, Li, Chunyuan, Li, Jinchao, Shayandeh, Shahin, Liden, Lars, Gao, Jianfeng
• Format: Journal Article
• Language: English
• Published: 02-08-2021
• ISSN: 2307-387X; 2307-387X
• DOI: 10.1162/tacl_a_00399

We present a new method, Soloist,1 that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i)Soloist creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, Soloist significantly outperforms existing methods; and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.