SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching

• URL: http://arxiv.org/abs/2005.05298v4
• Date: Fri, 9 Apr 2021 03:14:57 GMT
• Title: SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching
• Authors: Baolin Peng and Chunyuan Li and Jinchao Li and Shahin Shayandeh and Lars Liden and Jianfeng Gao
• Abstract summary: We parameterize modular task-oriented dialog systems using a Transformer-based auto-regressive language model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model. Experiments show that SOLOIST creates new state-of-the-art on well-studied task-oriented dialog benchmarks.
• Score: 81.45928589522032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new method SOLOIST 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.

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