From Natural Language Specifications to Program Input Parsers

By Tao Lei, Fan Long, Regina Barzilay, Martin C. Rinard. 2013

48 citations recorded by the source corpus.

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In Natural Language Processing

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.”
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).”
Summarizing Source Code using a Neural Attention Model (2016, methodology): “Similarly, Lei et al. (2013) interpret NL instructions to machine-executable code, and Kushman and Barzilay (2013) convert language to regular expressions.”
Bringing Machine Learning and Compositional Semantics Together (2015, background): “2013), interpreting natural language for performing programming tasks (Kushman & Barzilay 2013; Lei et al. 2013), playing computer games (Branavan et al.”
Machine Translation from Natural Language to Code using Long-Short Term Memory (2019, methodology): “The model produces a parser in C++ which can successfully parse more than 70% of the textual description of input [7].”
In-IDE Code Generation from Natural Language: Promise and Challenges (2021, background): “Examples include generating formatted input file parsers [61]; structured, idiomatic sequences of API calls [95]; regular expressions [58, 72, 87]; string manipulation DSL programs [99]; card implementations for trading card games [66]; and solutions to the simplest of programming competition-style ”
From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems (2017, background): “, 2010; Chen and Mooney, 2011, inter alia), or even general purpose programs (Lei et al., 2013; Ling et al., 2016; Yin and Neubig, 2017; Ling et al., 2017).”
Program Synthesis from Natural Language Using Recurrent Neural Networks (2017, background): “Recently, machine learning techniques have been developed to build probabilistic models to represent the joint distribution of text and programs [5, 10, 13] or the joint distribution of text and some formal specications [18].”
Retrieval-Based Neural Code Generation (2018, methodology): “For general purpose code generation, some data-driven work has been done for predicting input parsers (Lei et al., 2013) or a set of relevant methods (Raghothaman et al., 2016).”