AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
Abstract
Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
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 templatebased 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.
Results and benchmarks
We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
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Time to first repro: a few days
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Reproduction readiness
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Hardware requirements
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Validation caveat
Hugging Face artifacts
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Research context
33
Citations
61
References
Tasks
Computer science, Paraphrase, Parsing, Generality, Database schema, Training set, Logical form, Database
Methods
Schema (genetic algorithms), Information retrieval
Domains
Natural language processing, Artificial intelligence
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