%0 Conference Proceedings %T AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data %A Xu, Silei %A Semnani, Sina %A Campagna, Giovanni %A Lam, Monica %Y Webber, Bonnie %Y Cohn, Trevor %Y He, Yulan %Y Liu, Yang %S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) %D 2020 %8 November %I Association for Computational Linguistics %C Online %F xu-etal-2020-autoqa %X We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations. It uses automatic paraphrasing combined with template-based parsing to find alternative expressions of an attribute in different parts of speech. It also uses a novel filtered auto-paraphraser to generate correct paraphrases of entire sentences. We apply AutoQA to the Schema2QA dataset and obtain an average logical form accuracy of 62.9% when tested on natural questions, which is only 6.4% lower than a model trained with expert natural language annotations and paraphrase data collected from crowdworkers. To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset. AutoQA achieves 69.8% answer accuracy, 16.4% higher than the state-of-the-art zero-shot models and only 5.2% lower than the same model trained with human data. %R 10.18653/v1/2020.emnlp-main.31 %U https://aclanthology.org/2020.emnlp-main.31 %U https://doi.org/10.18653/v1/2020.emnlp-main.31 %P 422-434