Diverse data formats and ontologies of task-oriented dialogue (TOD) datasets hinder us from developing general dialogue models that perform well on many datasets and studying knowledge transfer between datasets. To address this issue, we present ConvLab-3, a flexible dialogue system toolkit based on a unified TOD data format. In ConvLab-3, different datasets are transformed into one unified format and loaded by models in the same way. As a result, the cost of adapting a new model or dataset is significantly reduced. Compared to the previous releases of ConvLab (Lee et al., 2019b; Zhu et al., 2020b), ConvLab-3 allows developing dialogue systems with much more datasets and enhances the utility of the reinforcement learning (RL) toolkit for dialogue policies. To showcase the use of ConvLab-3 and inspire future work, we present a comprehensive study with various settings. We show the benefit of pre-training on other datasets for few-shot fine-tuning and RL, and encourage evaluating policy with diverse user simulators.
翻译:为解决这一问题,我们提出ConvLab-3,一个基于统一的TOD数据格式的灵活对话系统工具包。在ConvLab-3, 不同的数据集被转换为统一格式,以同样的方式由模型装载。因此,调整新的模型或数据集的成本大大降低。与以前发行的ConvLab(Lee等人,2019b;Zhu等人,2020b)相比,ConvLab-3允许开发更多数据集的对话系统,并增强强化学习工具包对对话政策的效用。为了展示ConvLab-3的使用情况,并激励今后的工作,我们提出与不同环境的全面研究。我们展示了对其他数据集进行微调和RL的预先培训的好处,并鼓励与不同用户模拟器一起评价政策。