Using Semantic Unification to Generate Regular Expressions from Natural Language

By Nate Kushman, Regina Barzilay. 2013

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Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, background): “, 2011), previous work has looked at interpreting natural language for performing programming tasks (Kushman and Barzilay, 2013; Lei et al., 2013), playing computer games (Branavan et al.”
A Survey of Machine Learning for Big Code and Naturalness (2017, background): “Research on Source Code Generating Models PD (c|C (c)) (Sorted Alphabetically), Describing the Process of Generating Source Code Reference Type Representation PD Application Aggarwal et al. [2] PD (c |s) Token Phrase Migration Allamanis and Sutton [11] PD (c) Token n-gram — Allamanis et al. [5] PD (”
A Retrieve-and-Edit Framework for Predicting Structured Outputs (2018, background): “Code generation is well studied [21, 17, 4, 23, 1], but these approaches have not explored edit-based generation.”
Learning with Latent Language (2017, neutral): “While string editing tasks of the kind shown in Figure 5 are popular in both the programming by demonstration literature (Singh and Gulwani, 2012) and the semantic parsing literature (Kushman and Barzilay, 2013), we are unaware of any datasets that support both learning paradigms at the same time.”
Learning to Solve Arithmetic Word Problems with Verb Categorization (2014, background): “More recent work (Eisenstein et al., 2009; Kate and Mooney, 2007; Goldwasser and Roth, 2011; Poon and Domingos, 2009; Goldwasser et al., 2011; Kushman and Barzilay, 2013) reduce the amount of required supervision in mapping sentences to meaning representations while taking advantage of special…”
Learning to Automatically Solve Algebra Word Problems (2014, background): “, 2012; Artzi and Zettlemoyer, 2013), and program executions (Kushman and Barzilay, 2013; Lei et al., 2013).”
Latent Predictor Networks for Code Generation (2016, methodology): “Recently proposed models focus on Combinatory Categorical Grammars (Kushman and Barzilay, 2013; Artzi et al., 2015), Bayesian Tree Transducers (Jones et al., 2012; Lei et al., 2013) and Probabilistic Con-text Free Grammars (Andreas et al., 2013).”
Data Recombination for Neural Semantic Parsing (2016, background): “…many applications, including question answering (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Zettlemoyer and Collins, 2007; Liang et al., 2011; Berant et al., 2013), instruction following (Artzi and Zettlemoyer, 2013b), and regular expression generation (Kushman and Barzilay, 2013).”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, background): “, 2012; Krishnamurthy and Kollar, 2013), interpreting instructions (Chen and Mooney, 2011; Artzi and Zettlemoyer, 2013), and generating programs (Kushman and Barzilay, 2013).”
Building a Semantic Parser Overnight (2015, background): “By mapping natural language utterances to executable logical forms, semantic parsers have been useful for a variety of applications requiring precise language understanding (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Liang et al., 2011; Berant et al., 2013; Kwiatkowski et al., 2013; Artz”
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System (2018, background): “(Kushman and Barzilay, 2013; Locascio et al., 2016) formally verify the equivalence of different regular expressions by converting them to minimal deterministic ﬁnite automaton (DFAs).”
Learning executable semantic parsers for natural language understanding (2016, methodology): “The best reported result is based on semantic parsing and obtains 84.6% The Regexp824 dataset [17] consists of 824 natural language and regular expression pairs (e.g., “ three letter word starting with ’X’ ”).”
JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation (2019, background): “, 2013), regular expressions (Kushman and Barzilay, 2013; Locascio et al., 2016), database queries (Iyer et al.”
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (2019, background): “, 2010), lambda calculus (Zettlemoyer and Collins, 2005; Kushman and Barzilay, 2013), dependencybased compositional semantics (Liang et al.”
Naturalizing a Programming Language via Interactive Learning (2017, background): “, 2013), manipulating text (Kushman and Barzilay, 2013), or controlling the Internet of Things (Campagna et al.”
CodeGen-Test: An Automatic Code Generation Model Integrating Program Test Information (2022, methodology): “The early approaches are mainly based on templates [1], [2], [3], [4].”
Bimodal Modelling of Source Code and Natural Language (2015, background): “We note Kushman & Barzilay (2013) weaken the assumption that language and target are well-aligned in a natural language to regular expression application, but the method is specific to regular expressions, and the mapping is still more aligned than in our setting.”