E-commerce customers frequently seek detailed product information for purchase decisions, commonly contacting sellers directly with extended queries. This manual response requirement imposes additional costs and disrupts buyer's shopping experience with response time fluctuations ranging from hours to days. We seek to automate buyer inquiries to sellers in a leading e-commerce store using a domain-specific federated Question Answering (QA) system. The main challenge is adapting current QA systems, designed for single questions, to address detailed customer queries. We address this with a low-latency, sequence-to-sequence approach, MESSAGE-TO-QUESTION ( M2Q ). It reformulates buyer messages into succinct questions by identifying and extracting the most salient information from a message. Evaluation against baselines shows that M2Q yields relative increases of 757% in question understanding, and 1,746% in answering rate from the federated QA system. Live deployment shows that automatic answering saves sellers from manually responding to millions of messages per year, and also accelerates customer purchase decisions by eliminating the need for buyers to wait for a reply

翻译：电子商务客户在做出购买决策时经常寻求详细的产品信息，通常会向卖家发送长篇查询。这种手动回复要求带来了额外成本，且因回复时间从几小时到几天不等而干扰买家的购物体验。我们旨在利用一种特定领域的联邦问答（QA）系统，在领先的电子商务平台中自动化处理买家的查询。主要挑战在于将当前为单一问题设计的问答系统适配为处理详细的客户查询。我们通过一种低延迟的序列到序列方法MESSAGE-TO-QUESTION（M2Q）解决此问题。该方法通过从消息中识别和提取最显著的信息，将买家消息重新表述为简洁的问题。与基线方法的评估表明，M2Q在问题理解方面相对提升757%，在联邦问答系统的回答率方面相对提升1746%。实际部署显示，自动回答使卖家每年免于手动回复数百万条消息，同时通过消除买家等待回复的需求，加速了客户的购买决策。