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Computer Science > Computation and Language

arXiv:2208.14652 (cs)
[Submitted on 31 Aug 2022]

Title:Unified Knowledge Prompt Pre-training for Customer Service Dialogues

Authors:Keqing He, Jingang Wang, Chaobo Sun, Wei Wu
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Abstract: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.
Comments: CIKM2022
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2208.14652 [cs.CL]
  (or arXiv:2208.14652v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2208.14652

Submission history

From: Keqing He [view email]
[v1] Wed, 31 Aug 2022 06:23:53 UTC (125 KB)
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