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Global Reasoning over Database Structures for Text-to-SQL Parsing
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
State-of-the-art semantic parsers rely on autoregressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zeroshot), the parser often struggles to select the correct set of database constants in the new database, due to the local nature of decoding. In this work, we propose a semantic parser that globally reasons about the structure of the output query to make a more contextuallyinformed selection of database constants. We use
doi:10.18653/v1/d19-1378
dblp:conf/emnlp/BoginGB19
fatcat:xsfwd3ub65fudhupnhe3iumcla