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Learning an Executable Neural Semantic Parser
2018
Computational Linguistics
This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach which combines a generic tree-generation algorithm with domain-general grammar defined by the logical language. The generation process is modeled by structured recurrent neural networks,
doi:10.1162/coli_a_00342
fatcat:smuzyli3hvfd3knkzgbb64ppf4