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.
@article{arxiv.2003.08553,
title = {QnAMaker: Data to Bot in 2 Minutes},
author = {Parag Agrawal and Tulasi Menon and Aya Kamel and Michel Naim and Chaikesh Chouragade and Gurvinder Singh and Rohan Kulkarni and Anshuman Suri and Sahithi Katakam and Vineet Pratik and Prakul Bansal and Simerpreet Kaur and Neha Rajput and Anand Duggal and Achraf Chalabi and Prashant Choudhari and Reddy Satti and Niranjan Nayak},
journal= {arXiv preprint arXiv:2003.08553},
year = {2020}
}
Comments
Published at The Web Conference 2020 in the demo track