CARE: A Clue-guided Assistant for CSRs to Read User Manuals
Weihong Du, Jia Liu, Zujie Wen, Dingnan Jin, Hongru Liang, Wenqiang, Lei

TL;DR
CARE is a clue-guided reading assistant that helps customer service representatives quickly find accurate responses from user manuals, significantly reducing reading time while maintaining high service quality.
Contribution
The paper introduces CARE, a novel clue-guided assistant that leverages explicit clue chains and self-supervised learning to improve manual reading efficiency for CSRs.
Findings
Over 35% reduction in reading time for CSRs.
Maintains high response accuracy with >0.75 ICC score.
Effective in both offline and online scenarios.
Abstract
It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially information-rich ones. Current solutions don't fit the online custom service scenarios well due to the lack of attention to user questions and possible responses. Hence, we propose to develop a time-saving and careful reading assistant for CSRs, named CARE. It can help the CSRs quickly find proper responses from the user manuals via explicit clue chains. Specifically, each of the clue chains is formed by inferring over the user manuals, starting from the question clue aligned with the user question and ending at a possible response. To overcome the shortage of supervised data, we adopt the self-supervised strategy for model learning. The offline experiment shows that CARE is efficient in automatically inferring accurate responses from the user manual. The…
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Code & Models
Videos
Taxonomy
TopicsBusiness Process Modeling and Analysis · Semantic Web and Ontologies
MethodsSoftmax · travel james · Attention Is All You Need
