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

arXiv: https://arxiv.org/abs/2001.05609 · HTML: https://mlsanity.com/paper/2001.05609/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 12 of 12)

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- [SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models](https://mlsanity.com/paper/2311.09818/) (2023)
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- [Paraphrasing Techniques for Maritime QA system](https://mlsanity.com/paper/2203.10854/) (2022)
- [XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations](https://mlsanity.com/paper/2306.04085/) (2023)
- [ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models](https://mlsanity.com/paper/2306.09649/) (2023)
- [Discrete Auto-regressive Variational Attention Models for Text Modeling](https://mlsanity.com/paper/2106.08571/) (2021)
- [Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata](https://mlsanity.com/paper/2305.14202/) (2023)

## Abstract (arXiv)

Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can generate a Q&A system from a database schema augmented with a few annotations for each field. The key concept is to cover the space of possible compound queries on the database with a large number of in-domain questions synthesized with the help of a corpus of generic query templates. The synthesized data and a small paraphrase set are used to train a novel neural network based on the BERT pretrained model. We use Schema2QA to generate Q&A systems for five Schema.org domains, restaurants, people, movies, books and music, and obtain an overall accuracy between 64% and 75% on crowdsourced questions for these domains. Once annotations and paraphrases are obtained for a Schema.org schema, no additional manual effort is needed to create a Q&A agent for any website that uses the same schema. Furthermore, we demonstrate that learning can be transferred from the restaurant to the hotel domain, obtaining a 64% accuracy on crowdsourced questions with no manual effort. Schema2QA achieves an accuracy of 60% on popular restaurant questions that can be answered using Schema.org. Its performance is comparable to Google Assistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms all these assistants by at least 18% on more complex, long-tail questions.