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Previous works on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences.
Evaluating the state-of-the-art of end-to-end natural language generation: The E2E NLG Challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2019 · 1901
Earlier work this paper cites.
Few-shot NLG with pre-trained language model
Zhiyu Chen, Harini Eavani, Yinyin Liu, and William Yang Wang. 2019b · 1904
Earlier work this paper cites.
Open sesame: Getting inside bert’s linguistic knowledge
Yongjie Lin, Yi Chern Tan, and Robert Frank. 2019 · 1906
Earlier work this paper cites.
Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2019a · 1909
Earlier work this paper cites.
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. 2019 · 1910
Earlier work this paper cites.
Sticking to the facts: Confident decoding for faithful data-to-text generation
Ran Tian, Shashi Narayan, Thibault Sellam, and Ankur P. Parikh. 2019 · 1910
Earlier work this paper cites.
An algorithm for generation in unification categorial grammar
Jonathan Calder, Mike Reape, and Henk Zeevat. 1989 · 1989
Earlier work this paper cites.
Generating from a deep structure
Claire Gardent and Agnes Plainfossé. 1990 · 1990
Earlier work this paper cites.
Semantic-head-driven generation
Stuart M. Shieber, Gertjan van Noord, Fernando C. N. Pereira, and Robert C. Moore. 1990 · 1990
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Generation of text from logical formulae
John D. Phillips. 1993 · 1993
Earlier work this paper cites.
Building applied natural language generation systems
Ehud Reiter and Robert Dale. 1997 · 1997
Earlier work this paper cites.
A primer in bertology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2002
Earlier work this paper cites.
Logical natural language generation from open-domain tables
Wenhu Chen, Jianshu Chen, Yu Su, Zhiyu Chen, and William Yang Wang. 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.
Gpt-too: A language-model-first approach for amr-to-text generation
Manuel Mager, Ramón Fernández Astudillo, Tahira Naseem, Md. Arafat Sultan, Young-Suk Lee, Radu Florian, and Salim Roukos. 2020 · 2005
Earlier work this paper cites.
Efficient realization of coordinate structures in combinatory categorial grammar
Michael White. 2006 · 2006
Cited alongside, same era.
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
Cited alongside, same era.
Learning semantic correspondences with less supervision
Percy Liang, Michael I. Jordan, and Dan Klein. 2009 · 2009
Cited alongside, same era.
Deepbank. a dynamically annotated treebank of the wall street journal
Dan Flickinger, Yi Zhang, and Valia Kordoni. 2012 · 2012
Cited alongside, same era.
Methods for exploring and mining tables on wikipedia
Chandra Sekhar Bhagavatula, Thanapon Noraset, and Doug Downey. 2013 · 2013
Cited alongside, same era.
The groningen meaning bank
Johan Bos. 2013 · 2013
Multiwoz - A large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gasic. 2018 · 2018
Later among the works it cites.
Unsupervised natural language generation with denoising autoencoders
Markus Freitag and Scott Roy. 2018 · 2018
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Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
Albert Gatt and Emiel Krahmer. 2018 · 2018
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Natural language generation for electronic health records
Scott H Lee. 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 graph convolutional encoders for structured data to text generation
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Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
Semantically conditioned lstm-based natural language generation for spoken dialogue systems
Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Pei-hao Su, David Vandyke, and Steve J. Young. 2015 · 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.
Semeval-2016 task 8: Meaning representation parsing
Jonathan May. 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.
The E2E dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondrej Dusek, and Verena Rieser. 2017 · 2017
Cited alongside, same era.
Diego Marcheggiani and Laura Perez-Beltrachini. 2018 · 2018
Later among the works it cites.
A graph-to-sequence model for amr-to-text generation
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 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.
Sql-to-text generation with graph-to-sequence model
Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, and Vadim Sheinin. 2018 · 2018
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.
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
Later among the works it cites.
Table-to-text generation with effective hierarchical encoder on three dimensions (row, column and time)
Heng Gong, Xiaocheng Feng, Bing Qin, and Ting Liu. 2019 · 2019
Later among the works it cites.
Automatically generating interesting facts from wikipedia tables
Flip Korn, Xuezhi Wang, You Wu, and Cong Yu. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
MASS: masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
Later among the works it cites.