Having a bot for seamless conversations is a much-desired feature that products and services today seek for their websites and mobile apps. These bots help reduce traffic received by human support significantly by handling frequent and directly answerable known questions. Many such services have huge reference documents such as FAQ pages, which makes it hard for users to browse through this data. A conversation layer over such raw data can lower traffic to human support by a great margin. We demonstrate QnAMaker, a service that creates a conversational layer over semi-structured data such as FAQ pages, product manuals, and support documents. QnAMaker is the popular choice for Extraction and Question-Answering as a service and is used by over 15,000 bots in production. It is also used by search interfaces and not just bots.

中文翻译：

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QnAMaker：2 分钟内将数据传送到机器人

拥有用于无缝对话的机器人是当今产品和服务为其网站和移动应用程序寻求的一项非常需要的功能。这些机器人通过处理频繁且可直接回答的已知问题，帮助显着减少人工支持接收的流量。许多此类服务都有大量的参考文档，例如常见问题解答页面，这使得用户很难浏览这些数据。在此类原始数据上的对话层可以大大降低人类支持的流量。我们演示了 QnAMaker，这是一项在半结构化数据（如常见问题页面、产品手册和支持文档）上创建对话层的服务。QnAMaker 是提取和问答即服务的流行选择，在生产中被 15,000 多个机器人使用。它也被搜索界面使用，而不仅仅是机器人。