Dialog systems have achieved significant progress and have been widely used
in various scenarios. The previous researches mainly focused on designing
dialog generation models in a single scenario, while comprehensive abilities
are required to handle tasks under various scenarios in the real world. In this
paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which
can be applied in different dialog tasks (e.g. knowledge grounded dialog and
persona based dialog). Specifically, we propose a transferable response
generator pre-trained on diverse large-scale dialog corpora as the backbone of
MSDF, consisting of BERT-based encoders and a GPT-based decoder. To select the
response consistent with dialog history, we propose a consistency selector
trained through negative sampling. Moreover, the flexible copy mechanism of
external knowledge is also employed to enhance the utilization of multiform
knowledge in various scenarios. We conduct experiments on knowledge grounded
dialog, recommendation dialog, and persona based dialog tasks. The experimental
results indicate that our MSDF outperforms the baseline models with a large
margin. In the Multi-skill Dialog of 2021 Language and Intelligence Challenge,
our general MSDF won the 3rd prize, which proves our MSDF is effective and
competitive.

该研究提出了一种通用的多技能对话框架 MSDF, 它可以应用于不同的对话任务，并通过在不同的大型对话语料库上预训练响应生成器来实现。使用外部知识的灵活复制机制强化了在各种情境中的多种形式知识的利用，并且在多项任务中较基准模型表现更好。