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NLPRO SIGNED

Natural Language Programming: Turning Text into Executable Code

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 NLPRO project word cloud

Explore the words cloud of the NLPRO project. It provides you a very rough idea of what is the project "NLPRO" about.

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Project "NLPRO" data sheet

The following table provides information about the project.

Coordinator
BAR ILAN UNIVERSITY 

Organization address
address: BAR ILAN UNIVERSITY CAMPUS
city: RAMAT GAN
postcode: 52900
website: www.biu.ac.il

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Israel [IL]
 Total cost 1˙449˙375 €
 EC max contribution 1˙449˙375 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-STG
 Funding Scheme ERC-STG
 Starting year 2016
 Duration (year-month-day) from 2016-08-01   to  2022-07-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    BAR ILAN UNIVERSITY IL (RAMAT GAN) coordinator 1˙089˙199.00
2    THE OPEN UNIVERSITY IL (RAANANA) participant 360˙175.00

Map

 Project objective

Can we program computers in our native tongue? This idea, termed natural language programming, has attracted attention almost since the inception of computers themselves. From the point of view of software engineering (SE), efforts to program in natural language (NL) have relied thus far on controlled natural languages (CNL) -- small unambiguous fragments of English with strict grammars and limited expressivity. Is it possible to replace CNLs with truly natural, human language? From the point of view of natural language processing (NLP), current technology successfully extracts static information from NL texts. However, human-like NL understanding goes far beyond such extraction -- it requires dynamic interpretation processes which affect, and are affected by, the environment, update states and lead to action. So, is it possible to endow computers with this kind of dynamic NL understanding? These two questions are fundamental to SE and NLP, respectively, and addressing each requires a huge leap forward in the respective field. In this proposal I argue that the solutions to these seemingly separate challenges are actually closely intertwined, and that one community's challenge is the other community's stepping stone for a huge leap, and vice versa. Specifically, I propose to view executable programs in SE as semantic structures in NLP, and use them as the basis for broad-coverage dynamic semantic parsing. My ambitious, cross-disciplinary goal is to develop a new NL compiler based on this novel approach to NL semantics. The NL compiler will accept an NL description as input and return an executable system as output. Moreover, it will continuously improve its NL understanding capacity via online learning that will feed on verification, simulation, synthesis or user feedback. Such dynamic, ever-improving, NL compilers will have vast applications in AI, SE, robotics and cognitive computing and will fundamentally change the way humans and computers interact.

 Publications

year authors and title journal last update
List of publications.
2019 Tzuf Paz-Argaman, Reut Tsarfaty
RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation
published pages: 6448-6454, ISSN: , DOI: 10.18653/v1/d19-1681
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2020-04-03
2019 Tzuf Paz-Argaman
Natural Language Navigation
published pages: , ISSN: , DOI:
2020-02-27
2017 Tomer Cagan, Stefam Frank, Reut Tsarfaty
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)
published pages: , ISSN: , DOI:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics 2020-02-27
2018 More , Amir; Çetinoğlu , Özlem; Çöltekin , Çağri; Habash , Nizar; Sagot , Benoît; Seddah , Djamé; Taji , Dima; Tsarfaty , Reut
CoNLL-UL: Universal Morphological Lattices for Universal Dependency Parsing
published pages: , ISSN: , DOI:
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018) 2020-02-27
2018 Amit Seker, Amir More, Reut Tsarfaty
Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the {ONLP} Lab Submission to the CoNLL 2018 Shared Task
published pages: , ISSN: , DOI:
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies 2020-02-27
2018 Reut Tsarfaty
The Natural Language Programming {(NLPRO)} Project: Turning Text into Executable Code
published pages: , ISSN: , DOI:
Proceedings of REFSQ-2018 Workshops 2020-02-27
2017 Amir More, Reut Tsarfaty
Universal Joint Morph-Syntactic Processing: The Open University of Israel\'s Submission to The CoNLL 2017 Shared Task
published pages: , ISSN: , DOI:
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies 2020-02-27
2018 Adam Amram, Anat ben-David, reut Tsarfaty
Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew
published pages: , ISSN: , DOI:
Proceedings of the 27th International Conference on Computational Linguistics 2020-02-27

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