Fetching the paper…
Reading the bibliography…
Formal methods provide very powerful tools and techniques for the design and analysis of complex systems.
The temporal logic of programs
Amir Pnueli · 1977
Earlier work this paper cites.
Assisting requirement formalization by means of natural language translation
Alessandro Fantechi, Stefania Gnesi, Gioia Ristori, Michele Carenini, Massimo Vanocchi, and Paolo Moreschini · 1994
Earlier work this paper cites.
Automatic translation of natural language system specifications
Rani Nelken and Nissim Francez · 1996
Earlier work this paper cites.
Learning to parse database queries using inductive logic programming
John M. Zelle and Raymond J. Mooney · 1996
Earlier work this paper cites.
Patterns in property specifications for finite-state verification
Matthew B. Dwyer, George S. Avrunin, and James C. Corbett · 1999
Earlier work this paper cites.
Automated construction of database interfaces: Intergrating statistical and relational learning for semantic parsing
Lappoon R. Tang and Raymond J. Mooney · 2000
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Real-time specification patterns
Sascha Konrad and Betty H. C. Cheng · 2005
Earlier work this paper cites.
Learning for semantic parsing with statistical machine translation
Yuk Wah Wong and Raymond J. Mooney · 2006
Earlier work this paper cites.
Using string-kernels for learning semantic parsers
Rohit J. Kate and Raymond J. Mooney · 2006
Earlier work this paper cites.
Translating structured english to robot controllers
Hadas Kress-Gazit, Georgios E. Fainekos, and George J. Pappas · 2008
Earlier work this paper cites.
Automated identification of LTL patterns in natural language requirements
Allen P. Nikora and Galen Balcom · 2009
Earlier work this paper cites.
What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution
Juraj Dzifcak, Matthias Scheutz, Chitta Baral, and Paul Schermerhorn · 2009
Earlier work this paper cites.
Translating between language and logic: What is easy and what is difficult
Aarne Ranta · 2011
Earlier work this paper cites.
Monitoring properties of analog and mixed-signal circuits
Oded Maler and Dejan Ničković · 2013
Earlier work this paper cites.
Benchmarks for temporal logic requirements for automotive systems
Bardh Hoxha, Houssam Abbas, and Georgios E. Fainekos · 2014
Earlier work this paper cites.
Constructing an interactive natural language interface for relational databases
Fei Li and H. V. Jagadish · 2014
Earlier work this paper cites.
Powertrain control verification benchmark
Xiaoqing Jin, Jyotirmoy V Deshmukh, James Kapinski, Koichi Ueda, and Ken Butts · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Formal consistency checking over specifications in natural languages
Rongjie Yan, Chih-Hong Cheng, and Yesheng Chai · 2015
Cited alongside, same era.
Aligning qualitative, real-time, and probabilistic property specification patterns using a structured english grammar
Marco Autili, Lars Grunske, Markus Lumpe, Patrizio Pelliccione, and Antony Tang · 2015
Cited alongside, same era.
Provably correct reactive control from natural language
Constantine Lignos, Vasumathi Raman, Cameron Finucane, Mitchell P. Marcus, and Hadas Kress-Gazit · 2015
Cited alongside, same era.
Distributed communication-aware motion planning for multi-agent systems from stl and spatel specifications
Zhiyu Liu, Bo Wu, Jin Dai, and Hai Lin · 2017
Later among the works it cites.
Specification-based monitoring of cyber-physical systems: a survey on theory, tools and applications
Ezio Bartocci, Jyotirmoy Deshmukh, Alexandre Donzé, Georgios Fainekos, Oded Maler, Dejan Ničković, and Sriram Sankaranarayanan · 2018
Later among the works it cites.
Formal modelling of environment restrictions from natural-language requirements
Tainã Santos, Gustavo Carvalho, and Augusto Sampaio · 2018
Later among the works it cites.
NL2Bash: A corpus and semantic parser for natural language interface to the linux operating system
Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, and Michael D. Ernst · 2018
Later among the works it cites.
Subword regularization: Improving neural network translation models with multiple subword candidates
Taku Kudo · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Formal consistency checking over specifications in natural languages
Rongjie Yan, Chih-Hong Cheng, and Yesheng Chai · 2015
Cited alongside, same era.
Generating formal hardware verification properties from natural language documentation
Christopher B. Harris and Ian G. Harris · 2015
Cited alongside, same era.
Learning to generate pseudo-code from source code using statistical machine translation (T)
Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura · 2015
Cited alongside, same era.
Language to code: Learning semantic parsers for if-this-then-that recipes
Chris Quirk, Raymond Mooney, and Michel Galley · 2015
Cited alongside, same era.
ARSENAL: automatic requirements specification extraction from natural language
Shalini Ghosh, Daniel Elenius, Wenchao Li, Patrick Lincoln, Natarajan Shankar, and Wilfried Steiner · 2016
Cited alongside, same era.
Cornell SPF: Cornell semantic parsing framework, 2016
Yoav Artzi · 2016
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Cited alongside, same era.
Synthesis of LTL formulas from natural language texts: State of the art and research directions
Andrea Brunello, Angelo Montanari, and Mark Reynolds · 2019
Later among the works it cites.
Experience paper: Search-based testing in automated driving control applications
Christoph Gladisch, Thomas Heinz, Christian Heinzemann, Jens Oehlerking, Anne von Vietinghoff, and Tim Pfitzer · 2019
Later among the works it cites.
A survey on recent advances in named entity recognition from deep learning models
Vikas Yadav and Steven Bethard · 2019
Later among the works it cites.
Model-based reinforcement learning from signal temporal logic specifications
Parv Kapoor, Anand Balakrishnan, and Jyotirmoy V Deshmukh · 2020
Later among the works it cites.
A survey of reinforcement learning with temporal logic rewards, 2020
Hsuan-Cheng Liao · 2020
Later among the works it cites.
Temporal-logic-based semantic fault diagnosis with time-series data from industrial internet of things
Gang Chen, Mei Liu, and Zhaodan Kong · 2020
Later among the works it cites.
A survey on deep learning for named entity recognition
Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li · 2020
Later among the works it cites.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang · 2020
Later among the works it cites.
Signal-based properties of cyber-physical systems: Taxonomy and logic-based characterization
Chaima Boufaied, Maris Jukss, Domenico Bianculli, Lionel Claude Briand, and Yago Isasi Parache · 2021
Closest in time.
Sempre: Semantic parsing with execution, Accessed 2021
2021
Closest in time.
Sippycup, Accessed 2021
2021
Closest in time.
Wenliang Liu and Calin Belta · 2021
Closest in time.
Aston Zhang, Zachary C Lipton, Mu Li, and Alexander J Smola · 2021
Closest in time.
A survey of data augmentation approaches for NLP
Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy · 2021
Closest in time.