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Building a question-answering agent currently requires large annotateddatasets, which are prohibitively expensive. This paper proposes Schema2QA, anopen-source toolkit that can generate a Q&A system from a database schemaaugmented with a few annotations for each field. The key concept is to coverthe space of possible compound queries on the database with a large number ofin-domain questions synthesized with the help of a corpus of generic querytemplates. The synthesized data and a small paraphrase set are used to train anovel neural network based on the BERT pretrained model. We use Schema2QA togenerate Q&A systems for five Schema.org domains, restaurants, people, movies,books and music, and obtain an overall accuracy between 64% and 75% oncrowdsourced questions for these domains. Once annotations and paraphrases areobtained for a Schema.org schema, no additional manual effort is needed tocreate a Q&A agent for any website that uses the same schema. Furthermore, wedemonstrate that learning can be transferred from the restaurant to the hoteldomain, obtaining a 64% accuracy on crowdsourced questions with no manualeffort. Schema2QA achieves an accuracy of 60% on popular restaurant questionsthat can be answered using Schema.org. Its performance is comparable to GoogleAssistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms allthese assistants by at least 18% on more complex, long-tail questions.

Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web