NLP (Natural Language Processing) for NLP (Natural Language Programming)

By Rada Mihalcea, Hugo Liu, Henry Lieberman. 2006

104 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Code Generation & Program Synthesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Graham Neubig
2. Frank F. Xu
3. Bogdan Vasilescu
4. Regina Barzilay
5. Siddharth Garg
6. Nate Kushman
7. Brendan Dolan-Gavitt
8. Luke S. Zettlemoyer

In Information Systems

1. Frank F. Xu
2. Graham Neubig
3. Siddharth Garg
4. Bogdan Vasilescu
5. Ramesh Karri
6. Hammond A. Pearce
7. Brendan Dolan-Gavitt
8. Benjamin Tan

In Natural Language Processing

1. Graham Neubig
2. Frank F. Xu
3. Bogdan Vasilescu
4. Nate Kushman
5. Regina Barzilay
6. Percy Liang
7. Brendan Dolan-Gavitt
8. Benjamin Tan

In Code Generation & Program Synthesis

1. Frank F. Xu
2. Graham Neubig
3. Bogdan Vasilescu
4. Brendan Dolan-Gavitt
5. Benjamin Tan
6. Siddharth Garg
7. Ramesh Karri
8. Baleegh Ahmad

Citations in the corpus

• Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions (2021, background): “There are many efforts to automatically translate specifications into computer code for natural language programming [9], through formal models for automatic code generation (e.”
• DAVE: Deriving Automatically Verilog from English (2020, background): “Earlier work involves designing separate components to perform specific tasks such as identifying “steps”, “loops”, and “comments” from natural text [9].”
• An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions (2021, background): “There are many efforts to automatically translate specifications into computer code for natural language programming [9], through formal models for automatic code generation (e.g., [10], [11]) or via machine-learned NLP [12].”
• GitHub Copilot AI pair programmer: Asset or Liability? (2022, background): “One finds different approaches to automatic code generation in the literature, from natural language programming [23] and formal models [10, 19] to Evolutionary Algorithms [33] and machine-learned translation [28].”
• Using Semantic Unification to Generate Regular Expressions from Natural Language (2013, background): “…natural language to some form of meaning representation (Kate and Mooney, 2006; Kate et al., 2005; Raymond and Mooney, 2006; Thompson and Mooney, 2003; Wong and Mooney, 2006; Wong and Mooney, 2007; Zelle and Mooney, 1996; Branavan et al., 2009; Mihalcea et al., 2006; Poon and Domingos, 2009).”
• In-IDE Code Generation from Natural Language: Promise and Challenges (2021, background): “Researchers have proposed that natural language dialogue could be a new form of human-computer interaction since nearly the advent of modern computers [25, 33, 42, 74].”
• VeriGen: A Large Language Model for Verilog Code Generation (2023, background): “This motivates the use of natural language processing (NLP) to transform language to code [15].”