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：电子商务客户在购买决策过程中常需获取详细产品信息，通常直接向卖家发送长篇查询。这种人工回复需求增加了运营成本，且因回复时间波动（从数小时到数天）破坏了买家购物体验。我们提出在主流电商平台中采用领域特定的联邦问答系统来自动化处理买家向卖家发送的查询。主要挑战在于调整当前针对单一问题设计的问答系统，以应对客户详细查询。为此，我们提出一种低延迟的序列到序列方法——消息到问题（M2Q），通过识别并提取消息中最关键信息，将买家消息重构为简洁问题。与基线方法相比，M2Q在问题理解率上相对提升757%，在联邦问答系统的回答率上相对提升1746%。实际部署表明，自动回复系统每年可节省卖家数百万条消息的人工响应，并通过消除买家等待回复的需求，加速客户购买决策。