Dialogue bots have been widely applied in customer service scenarios to
provide timely and user-friendly experience. These bots must classify the
appropriate domain of a dialogue, understand the intent of users, and generate
proper responses. Existing dialogue pre-training models are designed only for
several dialogue tasks and ignore weakly-supervised expert knowledge in
customer service dialogues. In this paper, we propose a novel unified knowledge
prompt pre-training framework, UFA (\textbf{U}nified Model \textbf{F}or
\textbf{A}ll Tasks), for customer service dialogues. We formulate all the tasks
of customer service dialogues as a unified text-to-text generation task and
introduce a knowledge-driven prompt strategy to jointly learn from a mixture of
distinct dialogue tasks. We pre-train UFA on a large-scale Chinese customer
service corpus collected from practical scenarios and get significant
improvements on both natural language understanding (NLU) and natural language
generation (NLG) benchmarks.

本篇论文提出了一种创新的统一知识提示预训练框架 UFA（Unified Model For All Tasks），系统地解决了客户服务对话任务中的各种需求，通过混合不同的对话任务进行学习，显著提高了自然语言理解和生成的基准。