Transactions of the Association for Computational Linguistics |
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 (opens in new tab).
This repository showcases building task-oriented bot at scale with handful examples via fine-tuning a pretrained model using SOLOIST framework, and contains the dataset, source code and pre-trained model for the following paper: SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching Baolin Peng, Chunyuan Li, Jinchao Li, Shahin Shayandeh, Lars Liden, Jianfeng Gao Transactions of the Association for Computational Linguistics 2021
In this video tutorial, researchers demonstrate how pretrain grounded text generated (GTG) model can be finetuned and adapted to a specific task using conversation learner, a pretraining, finetuning and machine teaching framework to build task bots at scale.
