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Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019 · 1901
Earlier work this paper cites.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 1904
Earlier work this paper cites.
Design of a knowledge-based report generator
Karen Kukich. 1983 · 1983
Earlier work this paper cites.
Using natural-language processing to produce weather forecasts
E. Goldberg, N. Driedger, and R. I. Kittredge. 1994 · 1994
Earlier work this paper cites.
Realm: Retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002
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 · 2002
Earlier work this paper cites.
Entities as experts: Sparse memory access with entity supervision
Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, Eunsol Choi, and Tom Kwiatkowski. 2020 · 2004
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2004
Earlier work this paper cites.
Totto: A controlled table-to-text generation dataset
Ankur P Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020 · 2004
Earlier work this paper cites.
Text-to-text pre-training for data-to-text tasks
Mihir Kale. 2020 · 2005
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2005
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Dart: Open-domain structured data record to text generation
Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Nazneen Fatema Rajani, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, et al. 2020 · 2007
Earlier work this paper cites.
Facts as experts: Adaptable and interpretable neural memory over symbolic knowledge
Pat Verga, Haitian Sun, Livio Baldini Soares, and William W Cohen. 2020 · 2007
Earlier work this paper cites.
Open information extraction from the web
Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S Weld. 2008 · 2008
Earlier work this paper cites.
Global learning of focused entailment graphs
Jonathan Berant, Ido Dagan, and Jacob Goldberger. 2010 · 2010
Earlier work this paper cites.
Kgpt: Knowledge-grounded pre-training for data-to-text generation
Wenhu Chen, Yu Su, Xifeng Yan, and William Yang Wang. 2020 · 2010
Earlier work this paper cites.
Jaket: Joint pre-training of knowledge graph and language understanding
Donghan Yu, Chenguang Zhu, Yiming Yang, and Michael Zeng. 2020 · 2010
Earlier work this paper cites.
Representing general relational knowledge in ConceptNet 5
Robyn Speer and Catherine Havasi. 2012 · 2012
Cited alongside, same era.
Relation extraction with matrix factorization and universal schemas
Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M. Marlin. 2013 · 2013
Cited alongside, same era.
Wikidata: A free collaborative knowledge base
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Cited alongside, same era.
Leveraging linguistic structure for open domain information extraction
Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
Cited alongside, same era.
Neural text generation from structured data with application to the biography domain
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures
Thiago Castro Ferreira, Chris van der Lee, Emiel van Miltenburg, and Emiel Krahmer. 2019 · 2019
Later among the works it cites.
Scalable knowledge graph construction from text collections
Ryan Clancy, Ihab F. Ilyas, and Jimmy Lin. 2019 · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 2019
Later among the works it cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Later among the works it cites.
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Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
Cited alongside, same era.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Question answering on knowledge bases and text using universal schema and memory networks
Rajarshi Das, Manzil Zaheer, Siva Reddy, and Andrew McCallum. 2017 · 2017
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.
Challenges in data-to-document generation
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2017 · 2017
Cited alongside, same era.
Findings of the e2e nlg challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2018 · 2018
Cited alongside, same era.
T-REx: A large scale alignment of natural language with knowledge base triples
Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl. 2018 · 2018
Cited alongside, same era.
Barack’s wife hillary: Using knowledge graphs for fact-aware language modeling
Robert Logan, Nelson F. Liu, Matthew E. Peters, Matt Gardner, and Sameer Singh. 2019 · 2019
Later among the works it cites.
Black is to criminal as caucasian is to police: Detecting and removing multiclass bias in word embeddings
Thomas Manzini, Lim Yao Chong, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
Later among the works it cites.
Knowledge enhanced contextual word representations
Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019 · 2019
Later among the works it cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Later among the works it cites.
The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019 · 2019
Later among the works it cites.
PullNet: Open domain question answering with iterative retrieval on knowledge bases and text
Haitian Sun, Tania Bedrax-Weiss, and William Cohen. 2019 · 2019
Later among the works it cites.
Improving question answering over incomplete KBs with knowledge-aware reader
Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang. 2019 · 2019
Later among the works it cites.
Machine translation aided bilingual data-to-text generation and semantic parsing
Oshin Agarwal, Mihir Kale, Heming Ge, Siamak Shakeri, and Rami Al-Rfou. 2020 · 2020
Closest in time.
The 2020 bilingual, bi-directional WebNLG+ shared task: Overview and evaluation results (WebNLG+ 2020)
Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, and Anastasia Shimorina. 2020 · 2020
Closest in time.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Closest in time.
How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
Closest in time.