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The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including fully supervised training where annotated logical forms are given, weakly supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of data sets demonstrate the effectiveness of our parser.<\/jats:p>","DOI":"10.1162\/coli_a_00342","type":"journal-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T16:12:19Z","timestamp":1545408739000},"page":"59-94","source":"Crossref","is-referenced-by-count":12,"title":["Learning an Executable Neural Semantic Parser"],"prefix":"10.1162","volume":"45","author":[{"given":"Jianpeng","family":"Cheng","sequence":"first","affiliation":[{"name":"University of Edinburgh."}]},{"given":"Siva","family":"Reddy","sequence":"additional","affiliation":[{"name":"Stanford University."}]},{"given":"Vijay","family":"Saraswat","sequence":"additional","affiliation":[{"name":"IBM T.J. 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