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Translating Natural Language Queries (NLQs) to Structured Query Language(SQL) in interfaces deployed in relational databases is a challenging task,which has been widely studied in database community recently. Conventional rulebased systems utilize series of solutions as a pipeline to deal with each stepof this task, namely stop word filtering, tokenization, stemming/lemmatization,parsing, tagging, and translation. Recent works have mostly focused on thetranslation step overlooking the earlier steps by using ad-hoc solutions. Inthe pipeline, one of the most critical and challenging problems is keywordmapping; constructing a mapping between tokens in the query and relationaldatabase elements (tables, attributes, values, etc.). We define the keywordmapping problem as a sequence tagging problem, and propose a novel deeplearning based supervised approach that utilizes POS tags of NLQs. Our proposedapproach, called \textit{DBTagger} (DataBase Tagger), is an end-to-end andschema independent solution, which makes it practical for various relationaldatabases. We evaluate our approach on eight different datasets, and report newstate-of-the-art accuracy results, $92.4\%$ on the average. Our results alsoindicate that DBTagger is faster than its counterparts up to $10000$ times andscalable for bigger databases.

DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks