AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
Silei Xu, Sina J. Semnani, Giovanni Campagna, Monica S. Lam
Abstract
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.
Topics & Concepts
Computer scienceParaphraseParsingNatural language processingGeneralityArtificial intelligenceDatabase schemaSchema (genetic algorithms)Training setLogical formDatabaseNatural languageSet (abstract data type)Information retrievalDatabase designProgramming languagePsychologyPsychotherapistNatural Language Processing TechniquesTopic ModelingMachine Learning and Algorithms