Fetching the paper…
Reading the bibliography…
Beginning with McCarthy's Advice Taker (1959), AI has pursued the goal of providing a system with explicit, general knowledge and having the system reason over that knowledge.
Reasoning over paragraph effects in situations
Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner · 1908
Earlier work this paper cites.
The logic theory machine-a complex information processing system
Allen Newell and Herbert A. Simon · 1956
Earlier work this paper cites.
Programs with common sense
John W. McCarthy · 1959
Earlier work this paper cites.
Applications of circumscription to formalizing common sense knowledge
John McCarthy · 1984
Earlier work this paper cites.
Towards a theory of declarative knowledge
Krzysztof R. Apt, Howard A. Blair, and Adrian Walker · 1988
Earlier work this paper cites.
Of brittleness and bottlenecks: Challenges in the creation of pattern-recognition and expert-system models
Mark A Musen and Johan Van der Lei · 1988
Earlier work this paper cites.
General logical databases and programs: Default logic semantics and stratification
Nicole Bidoit and Christine Froidevaux · 1991
Earlier work this paper cites.
Smodels - an implementation of the stable model and well-founded semantics for normal lp
Ilkka Niemelä and Patrik Simons · 1997
Earlier work this paper cites.
Expert systems in production planning and scheduling: A state-of-the-art survey
Kostas S Metaxiotis, Dimitris Askounis, and John Psarras · 2002
Earlier work this paper cites.
Natural language inference
Christopher D. Manning and Bill MacCartney · 2009
Earlier work this paper cites.
Natural logic and semantics
Lawrence S Moss · 2010
Earlier work this paper cites.
Recognizing Textual Entailment: Models and Applications
Ido Dagan, Dan Roth, Mark Sammons, and Fabio Massimo Zanzotto · 2013
Earlier work this paper cites.
Natural logic and natural language inference
Bill MacCartney and Christopher D. Manning · 2014
Earlier work this paper cites.
NIPS 2016 tutorial: Generative adversarial networks
Ian J. Goodfellow · 2016
Earlier work this paper cites.
A decomposable attention model for natural language inference
Ankur P. Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Cited alongside, same era.
Towards AI-Complete question answering: A set of prerequisite toy tasks
J. Weston, A. Bordes, S. Chopra, and T. Mikolov · 2016
Cited alongside, same era.
Enhanced lstm for natural language inference
Qian Chen, Xiao-Dan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2017
Cited alongside, same era.
Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy · 2017
Cited alongside, same era.
Deep learning for symbolic mathematics
Guillaume Lample and Franccois Charton · 2019
Later among the works it cites.
RoBERTa: a robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Later among the works it cites.
Differentiable reasoning on large knowledge bases and natural language
Pasquale Minervini, Matko Bovsnjak, Tim Rocktaschel, Sebastian Riedel, and Edward Grefenstette · 2019
Later among the works it cites.
Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli · 2019
Later among the works it cites.
Learning a SAT solver from single-bit supervision
Daniel Selsam, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L. Dill · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, and Sebastian Riedel · 2018
Cited alongside, same era.
Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R. Bowman · 2018
Cited alongside, same era.
Improving machine reading comprehension with general reading strategies
Kai Sun, Dian Yu, Dong Yu, and Claire Cardie · 2018
Cited alongside, same era.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
Cited alongside, same era.
Improving graph neural network representations of logical formulae with subgraph pooling
Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth D. Forbus, and Achille Fokoue · 2019
Cited alongside, same era.
Han He and Jinho D. Choi · 2019
Cited alongside, same era.
Adaptive generation of programming puzzles
Ashwin Kalyan, Oleksandr Polozov, and Adam Kalai · 2019
Cited alongside, same era.
Clutrr: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton · 2019
Later among the works it cites.
Quartz: An open-domain dataset of qualitative relationship questions
Oyvind Tafjord, Matt Gardner, Kevin Lin, and Peter Clark · 2019
Later among the works it cites.
olmpics - on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant · 2019
Later among the works it cites.
Learning deep transformer models for machine translation
Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao · 2019
Later among the works it cites.
Nlprolog: Reasoning with weak unification for question answering in natural language
Leon Weber, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, and Tim Rocktäschel · 2019
Later among the works it cites.
Learning to reason: Leveraging neural networks for approximate dnf counting
Ralph Abboud, Ismail Ilkan Ceylan, and Thomas Lukasiewicz · 2020
Closest in time.
An experimental study of formula embeddings for automated theorem proving in first-order logic
Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse, and Achille Fokoue · 2020
Closest in time.
Probing natural language inference models through semantic fragments
Kyle Richardson, Hai Hu, Lawrence S Moss, and Ashish Sabharwal · 2020
Closest in time.