Fetching the paper…
Reading the bibliography…
Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data.
Natural language generation in health care
Alison J Cawsey, Bonnie L Webber, and Ray B Jones. 1997 · 1997
Earlier work this paper cites.
Building applied natural language generation systems
Ehud Reiter and Robert Dale. 1997 · 1997
Earlier work this paper cites.
Spot: A trainable sentence planner
Marilyn A. Walker, Owen Rambow, and Monica Rogati. 2001 · 2001
Earlier work this paper cites.
The development of a natural language generation system for personalized e-health information
Chrysanne DiMarco, HDominic Covvey, D Cowan, V DiCiccio, E Hovy, J Lipa, D Mulholland, et al. 2007 · 2007
Earlier work this paper cites.
Learning semantic correspondences with less supervision
Percy Liang, Michael I. Jordan, and Dan Klein. 2009 · 2009
Earlier work this paper cites.
Natural language generation with tree conditional random fields
Wei Lu, Hwee Tou Ng, and Wee Sun Lee. 2009 · 2009
Earlier work this paper cites.
Unsupervised concept-to-text generation with hypergraphs
Ioannis Konstas and Mirella Lapata. 2012 · 2012
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013 · 2013
Earlier work this paper cites.
A global model for concept-to-text generation
Ioannis Konstas and Mirella Lapata. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Neural generative question answering
Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, and Xiaoming Li. 2016 · 2015
Cited alongside, same era.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
On generating characteristic-rich question sets for QA evaluation
Zero-shot question generation from knowledge graphs for unseen predicates and entity types
Hady ElSahar, Christophe Gravier, and Frédérique Laforest. 2018 · 2018
Later among the works it cites.
Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
Albert Gatt and Emiel Krahmer. 2018 · 2018
Later among the works it cites.
A knowledge-grounded neural conversation model
Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, and Michel Galley. 2018 · 2018
Later among the works it cites.
Table-to-text generation by structure-aware seq2seq learning
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 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…
Yu Su, Huan Sun, Brian M. Sadler, Mudhakar Srivatsa, Izzeddin Gur, Zenghui Yan, and Xifeng Yan. 2016 · 2016
Cited alongside, same era.
The webnlg challenge: Generating text from RDF data
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017 · 2017
Cited alongside, same era.
Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings
He He, Anusha Balakrishnan, Mihail Eric, and Percy Liang. 2017 · 2017
Cited alongside, same era.
The E2E dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondrej Dusek, and Verena Rieser. 2017 · 2017
Cited alongside, same era.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Cited alongside, same era.
Challenges in data-to-document generation
Sam Wiseman, Stuart M. Shieber, and Alexander M. Rush. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Ratish Puduppully, Li Dong, and Mirella Lapata. 2018 · 2018
Later among the works it cites.
Complex sequential question answering: Towards learning to converse over linked question answer pairs with a knowledge graph
Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, and Sarath Chandar. 2018 · 2018
Later among the works it cites.
Learning neural templates for text generation
Sam Wiseman, Stuart M. Shieber, and Alexander M. Rush. 2018 · 2018
Later among the works it cites.
Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur P. Parikh, Ming-Wei Chang, Dipanjan Das, and William W. Cohen. 2019 · 2019
Closest in time.
Clinical natural language processing with deep learning
Sadid A Hasan and Oladimeji Farri. 2019 · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Closest in time.
Key fact as pivot: A two-stage model for low resource table-to-text generation
Shuming Ma, Pengcheng Yang, Tianyu Liu, Peng Li, Jie Zhou, and Xu Sun. 2019 · 2057
Closest in time.