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：为客服人员构建用户手册阅读助手，尤其是在处理信息密集型手册时，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潜在回复，难以适配在线客服场景。为此，我们提出开发一款面向客服人员的省时且细致的阅读助手CARE。该助手可通过显式线索链，帮助客服人员从用户手册中快速定位恰当回复。具体而言，每条线索链均通过对用户手册进行推理构建：始于与用户问题对齐的提问线索，止于可能的回复。为缓解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能高效地从用户手册中自动推理出准确回复。在线实验进一步证明，CARE在减轻客服人员阅读负担并保持高服务质量方面具有显著优势，具体表现为耗时降低超过35%，同时保持高于0.75的ICC评分。