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 online experiment further demonstrates the superiority of CARE to reduce CSRs' reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score.

翻译：为客服代表（CSRs）构建用户手册阅读助手，尤其是在处理信息密集的手册时，能够显著节省时间。现有解决方案因未能充分关注用户问题与可能的回复，难以很好地适应在线客服场景。为此，我们提出开发一款面向客服代表的省时且细致的阅读助手，命名为CARE。该助手能够通过显式的线索链，帮助客服代表快速从用户手册中找到合适的回复。具体而言，每条线索链均通过对用户手册进行推理构建而成，起始于与用户问题对齐的问题线索，终止于一个可能的回复。为克服监督数据不足的问题，我们采用自监督策略进行模型学习。离线实验表明，CARE能够高效地从用户手册中自动推理出准确的回复。在线实验进一步证明，CARE在减轻客服代表阅读负担并保持高服务质量方面具有优越性，具体表现为耗时减少超过35%，同时保持ICC分数大于0.75。