Fetching the paper…
Reading the bibliography…
Understanding and reasoning about cooking recipes is a fruitful research direction towards enabling machines to interpret procedural text.
A unified theory of inference for text understanding
Peter Norvig. 1987 · 1987
Earlier work this paper cites.
Natural language question answering: The view from here
Lynette Hirschman and Robert Gaizauskas. 2001 · 2001
Earlier work this paper cites.
Answering and questioning for machine reading
Lucy Vanderwende. 2007 · 2007
Earlier work this paper cites.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
Earlier work this paper cites.
langid.py
Marco Lui and Timothy Baldwin. 2012 · 2012
Earlier work this paper cites.
Towards the machine comprehension of text: An essay
Christopher JC Burges. 2013 · 2013
Earlier work this paper cites.
From machine learning to machine reasoning - an essay
Léon Bottou. 2014 · 2014
Earlier work this paper cites.
Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov. 2014 · 2014
Earlier work this paper cites.
Cooking with semantics
Jonathan Malmaud, Earl Wagner, Nancy Chang, and Kevin Murphy. 2014 · 2014
Earlier work this paper cites.
VQA: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Predicting the structure of cooking recipes
Jermsak Jermsurawong and Nizar Habash. 2015 · 2015
Earlier work this paper cites.
From word embeddings to document distances
Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, and Kilian Q. Weinberger. 2015 · 2015
Earlier work this paper cites.
What’s cookin’? Interpreting cooking videos using text, speech and vision
Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, and Kevin Murphy. 2015 · 2015
Cited alongside, same era.
ImageNet: Large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015 · 2015
Cited alongside, same era.
Unsupervised semantic parsing of video collections
Ozan Sener, Amir Zamir, Silvio Savarese, and Ashutosh Saxena. 2015 · 2015
Cited alongside, same era.
Visual madlibs: Fill in the blank description generation and question answering
Licheng Yu, Eunbyung Park, Alexander C. Berg, and Tamara L Berg. 2015 · 2015
Cited alongside, same era.
Sort story: Sorting jumbled images and captions into stories
Harsh Agrawal, Arjun Chandrasekaran, Dhruv Batra, Devi Parikh, and Mohit Bansal. 2016 · 2016
Cited alongside, same era.
MovieQA: Understanding stories in movies through question-answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. 2016 · 2016
Later among the works it cites.
Making the V in VQA matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
Later among the works it cites.
The amazing mysteries of the gutter: Drawing inferences between panels in comic book narratives
Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daumé III, and Larry Davis. 2017 · 2017
Later among the works it cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017 · 2017
Later among the works it cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Danqi Chen, Jason Bolton, and Christopher D Manning. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
WikiReading: A novel large-scale language understanding task over Wikipedia
Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot. 2016 · 2016
Cited alongside, same era.
The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016 · 2016
Cited alongside, same era.
A diagram is worth a dozen images
Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. 2016 · 2016
Cited alongside, same era.
Globally coherent text generation with neural checklist models
Chloé Kiddon, Luke Zettlemoyer, and Yejin Choi. 2016 · 2016
Cited alongside, same era.
MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Cited alongside, same era.
Are you smarter than a sixth grader? Textbook question answering for multimodal machine comprehension
Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
Later among the works it cites.
Learning cross-modal embeddings for cooking recipes and food images
Amaia Salvador, Nicholas Hynes, Yusuf Aytar, Javier Marin, Ferda Ofli, Ingmar Weber, and Antonio Torralba. 2017 · 2017
Later among the works it cites.
Prerequisite skills for reading comprehension: Multi-perspective analysis of mctest datasets and systems
Saku Sugawara, Hikaru Yokono, and Akiko Aizawa. 2017 · 2017
Later among the works it cites.
Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017 · 2017
Later among the works it cites.
Simulating action dynamics with neural process networks
Antoine Bosselut, Corin Ennis, Omer Levy, Ari Holtzman, Dieter Fox, and Yejin Choi. 2018 · 2018
Closest in time.
FigureQA: An annotated figure dataset for visual reasoning
Samira Ebrahimi Kahou, Adam Atkinson, Vincent Michalski, Akos Kadar, Adam Trischler, and Yoshua Bengio. 2018 · 2018
Closest in time.
The NarrativeQA reading comprehension challenge
Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
Closest in time.