论文标题
QNAMAKER：数据在2分钟内到机器人
QnAMaker: Data to Bot in 2 Minutes
论文作者
论文摘要
进行无缝对话的机器人是一项备受期待的功能，今天的产品和服务都为其网站和移动应用程序寻求。这些机器人有助于通过处理频繁且直接回答的已知问题来大大减少人类支持收到的流量。许多这样的服务都有大量的参考文档，例如常见问题页，这使用户很难浏览这些数据。对此类原始数据的对话层可以通过大幅度降低流量到人类支持。我们演示了Qnamaker，该服务可以通过半结构化数据（例如常见问题页面，产品手册和支持文档）创建对话层。 Qnamaker是提取和提问作为一项服务的流行选择，生产中有15,000多个机器人使用。搜索接口也不仅是机器人使用的。
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.
