Light RAT-SQL: A RAT-SQL with More Abstraction and Less Embedding of Pre-existing Relations (original) (raw)
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Deep neural semantic parsing: translating from natural language into SPARQL
I would like to thank my advisor Marcelo Finger for allowing me to explore new approaches to semantic parsing, for his support and constructive criticism of my work. I would like to thank all my friends and family who have been on my side during the development of this work. There are so many friends and loved ones that I will not even try to name just to not commit the rudeness of forgetting someone.
Modern natural language interfaces to databases
Proceedings of the 20th international conference on Computational Linguistics - COLING '04, 2004
Natural Language Interfaces to Databases (NLIs) can benefit from the advances in statistical parsing over the last fifteen years or so. However, statistical parsers require training on a massive, labeled corpus, and manually creating such a corpus for each database is prohibitively expensive. To address this quandary, this paper reports on the PRECISE NLI, which uses a statistical parser as a "plug in". The paper shows how a strong semantic model coupled with "light re-training" enables PRECISE to overcome parser errors, and correctly map from parsed questions to the corresponding SQL queries. We discuss the issues in using statistical parsers to build database-independent NLIs, and report on experimental results with the benchmark ATIS data set where PRECISE achieves 94% accuracy.