Learning an Executable Neural Semantic Parser

• Published in: Computational linguistics - Association for Computational Linguistics Vol. 45; no. 1; pp. 59 - 94
• Main Authors: Cheng, Jianpeng, Reddy, Siva, Saraswat, Vijay, Lapata, Mirella
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.03.2019; MIT Press Journals, The; The MIT Press
• Subjects: Algorithms; Knowledge bases (artificial intelligence); Natural language; Natural language processing; Neural networks; Parsing; Recurrent neural networks; Semantics; Sentences; Training; Tree generating algorithms
• ISSN: 0891-2017; 1530-9312
• DOI: 10.1162/coli_a_00342

This article describes a neural semantic parser that maps natural language utterances onto logical forms that 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, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language. 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.