Learning an Executable Neural Semantic Parser

By Jianpeng Cheng, Siva Reddy, Vijay A. Saraswat, Mirella Lapata. 2017

49 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021, background): “, 2019) or sequences of parse actions (Chen et al., 2018; Cheng et al., 2019; Lin et al., 2019; Zhang et al., 2019; Yin and Neubig, 2018; Chen et al., 2018; Guo et al., 2019; Wang et al., 2020a; Li et al., 2021).”
Value-Agnostic Conversational Semantic Parsing (2021, methodology): “Most such approaches use this structure as a constraint while decoding, ﬁlling in function arguments one-at-a-time, in either a top-down fashion (e.g., Dong and Lapata, 2016; Krishnamurthy et al., 2017) or a bottom-up fashion (e.g., Misra and Artzi, 2016; Cheng et al., 2018).”
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing (2020, background): “Bottom-up decoding in semantic parsing has received little attention (Cheng et al., 2019; Odena et al., 2020).”
Benchmarking Meaning Representations in Neural Semantic Parsing (2020, methodology): “First, for each natural language utter-ance in the three datasets, U NIMER provides annotated logical forms in four different MRs, including Prolog, Lambda Calculus, FunQL, and SQL.”
Few-Shot Semantic Parsing for New Predicates (2021, methodology): “The base architecture largely resembles (Cheng et al., 2019).”
Complexity-Based Prompting for Multi-Step Reasoning (2022, background): “The procedure of chain of thoughts prompting is conceptually similar to classical semantic parsing where one generates a logical form then executes it upon a knowledge base to reach a ﬁnal answer (Liang, 2016; Cheng et al., 2019).”