NLyze: interactive programming by natural language for spreadsheet data analysis and manipulation

By Sumit Gulwani, Mark Marron. 2014

131 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Software, Natural Language Processing

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Citations in the corpus

A Survey of Machine Learning for Big Code and Naturalness (2017, methodology): “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 (”
Summarizing Source Code using a Neural Attention Model (2016, methodology): “…n s(c, n) (1) RET We also use the scoring function s to retrieve the highest scoring code snippet c∗j from our training corpus, given a NL question n ∈ UN : c∗j = argmax cj s(cj , n), 1 ≤ j ≤ J (2) In this work, s is computed using an LSTM neural attention model, to be described in Section 5.”
Naturalizing a Programming Language via Interactive Learning (2017, methodology): “In tasks such as analyzing and plotting data (Gulwani and Marron, 2014), querying databases (Zelle and Mooney, 1996; Berant et al.”
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes (2015, background): “, 2013), and spreadsheets (Gulwani and Marron, 2014).”
Program Synthesis (2017, background): “The user intent can be expressed in various forms including logical specification, examples [44], traces, natural language [28, 46, 79], partial Full text available at: http://dx.”
SpreadsheetCoder: Formula Prediction from Semi-structured Context (2021, methodology): “…et al., 2012; Balog et al., 2017; Bunel et al., 2018; Bavishi et al., 2019; Shin et al., 2018; Chen et al., 2019), natural language descriptions (Gulwani & Marron, 2014; Yu et al., 2018; Yin et al., 2018; Lin et al., 2018; Liang et al., 2018; Wang et al., 2020), and images (Wu et al., 2017;…”
Building Natural Language Interfaces to Web APIs (2017, background): “On the otherhand, NL2APIs can alsofacilitate web service discovery [20], recommendation [35], and help API programming by reducing the burden to memorize the available web APIs and their syntax [10].”
Program Synthesis from Natural Language Using Recurrent Neural Networks (2017, methodology): “Rule-based approaches have been developed to synthesize SQL queries [18], SmartPhone scripts [17], Java method specications [25], and Spreadsheet programs [9].”
Robust Text-to-SQL Generation with Execution-Guided Decoding (2018, background): “Developing effective semantic parsers to translate natural language questions into logical programs has been a long-standing goal (Poon 2013; Zettlemoyer and Collins 2005; Pasupat and Liang 2015; Li, Yang, and Jagadish 2005; Gulwani and Marron 2014).”
LEVER: Learning to Verify Language-to-Code Generation with Execution (2023, unknown): “…including rule-based systems (Woods, 1973; Templeton & Burger, 1983), structured prediction (Zelle & Mooney, 1996; Zettlemoyer & Collins, 2005; Gulwani & Marron, 2014) and deep learning (Xiao et al., 2016; Dong & Lapata, 2016; Rabinovich et al., 2017; Zhong et al., 2017; Lin et al., 2017).”
Bimodal Modelling of Source Code and Natural Language (2015, background): “Gulwani & Marron (2014) synthesize a restricted domain-specific language for spreadsheets given a natural language query, using translation-inspired algorithms.”
Program Synthesis Using Natural Language (2015, methodology): “Again, NLyze is designed for a specific domain (spreadsheet formula) and uses a relatively simple ranking system consisting of only the equivalent of the coverage, mapping, and overlap features presented in our paper.”
SQLizer: query synthesis from natural language (2017, methodology): “…query synthesis, natural language has also been used as the preferred specification mechanism in the context of smartphone automation scripts [Le et al. 2013], “if-then-else recipes" [Quirk et al. 2015], spreadsheet programming [Gulwani and Marron 2014], and string manipulation [Raza et al. 2015].”