Language to Logical Form with Neural Attention

By Li Dong, Mirella Lapata. 2016

785 citations recorded by the source corpus.

Semantic parsing aims at mapping natural language to machine interpretable meaning representations. Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific. In this paper we present a general method based on an attention-enhanced encoder-decoder model. We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors. Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Learned in Translation: Contextualized Word Vectors (2017, background): “Attention mechanisms have also been successfully applied to NLP tasks like entailment [Conneau et al., 2017], summarization [Nallapati et al., 2016], question answering [Seo et al., 2017, Xiong et al., 2017, Min et al., 2017], and semantic parsing [Dong and Lapata, 2016].”
SPoC: Search-based Pseudocode to Code (2019, background): “Partial parses can be constructed by combining smaller partial parses in a bottom-up fashion [58, 28, 27], adding the next token to the sequence [10, 22, 51, 11, 25], or creating a new node in a tree structure [10, 26, 54, 39].”
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (2018, methodology): “Semantic parsers have been successfully trained using neural sequence-to-sequence models with a cross-entropy objective and question-parse pairs (Jia and Liang, 2016; Dong and Lapata, 2016)) or question-answer pairs (Neelakantan et al.”
Unsupervised Question Decomposition for Question Answering (2020, background): “, 1974), as well as neural Seq2Seq semantic parsers (Dong and Lapata, 2016) and neural module networks (Andreas et al.”
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017, methodology): “This model by Dong & Lapata (2016) achieves state of the art results on a variety of semantic parsing datasets despite not using hand-engineered grammar.”
Execution-Based Evaluation for Open-Domain Code Generation (2022, methodology): “Execution-based Evaluation Evaluation by execution has long been used for SQL (Zhong et al., 2017) or logical forms (Dong and Lapata, 2016).”
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (2018, methodology): “They have been studied extensively (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Wong and Mooney, 2007; Das et al., 2010; Liang et al., 2011; Banarescu et al., 2013; Artzi and Zettlemoyer, 2013; Reddy et al., 2014; Berant and Liang, 2014; Dong and Lapata, 2016).”
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (2016, methodology): “In semantic parsing, recent work handled this (Dong and Lapata, 2016; Jia and Liang, 2016) by training from manually annotated programs and avoiding program execution at training time.”
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision (2019, methodology): “Thus, NS-CL generates the hierarchies of latent programs in a sequence to tree manner (Dong & Lapata, 2016).”
TabFact: A Large-scale Dataset for Table-based Fact Verification (2019, background): “Recently, the first-ever end-to-end fact-checking system has been designed and proposed in Hassan et al. (2017). The verification problem has been extensively studied under different natural language tasks such as recognizing textual entailment (RTE) (Dagan et al.”
Abstract Syntax Networks for Code Generation and Semantic Parsing (2017, methodology): “Sequence-to-sequence models have proven effective for both tasks (Dong and Lapata, 2016; Ling et al., 2016), using encoder-decoder frameworks to exploit the sequential structure on both the input and output side.”
Learning a Neural Semantic Parser from User Feedback (2017, methodology): “Recently, neural models have been successfully applied to semantic parsing with simpler meaning representation languages (Dong and Lapata, 2016; Jia and Liang, 2016) and short regular expressions (Locascio et al.”
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System (2018, methodology): “We build on recent work in neural semantic parsing (Dong and Lapata, 2016; Ling et al., 2016), by evaluating the standard Seq2seq model (Sutskever et al., 2014) and the CopyNet model (Gu et al., 2016).”
Tree-to-tree Neural Networks for Program Translation (2018, methodology): “We evaluate our tree-to-tree model against a sequence-to-sequence model [4, 31], a sequence-to-tree model [11], and a tree-to-sequence model [13].”
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables (2017, methodology): “Recent work has shown that recurrent neural networks can be used for semantic parsing by encoding the question then predicting each token of the logical form in sequence (Jia and Liang, 2016; Dong and Lapata, 2016).”
JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation (2019, methodology): “Recent methods focus on constrained decoding mechanisms to generate syntactically correct output using a decoder that is either grammar-aware or has a dynamically-determined modular structure paralleling the structure of the abstract syntax tree of the code (Dong and Lapata, 2016; Rabinovich et al.,”
Coarse-to-Fine Decoding for Neural Semantic Parsing (2018, background): “, 2015) has provided strong impetus to treat semantic parsing as a sequence-to-sequence problem (Jia and Liang, 2016; Dong and Lapata, 2016; Ling et al., 2016).”