  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.
