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Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case.
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
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BLEU: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Europarl: A Parallel Corpus for Statistical Machine Translation
Philipp Koehn. 2005 · 2005
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Sentence compression by deletion with LSTMs
Katja Filippova, Enrique Alfonseca, Carlos A. Colmenares, Lukasz Kaiser, and Oriol Vinyals. 2015 · 2015
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tom´aˇs Koˇcisk´y, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Controlling output length in neural encoder-decoders
Yuta Kikuchi, Graham Neubig, Ryohei Sasano, Hiroya Takamura, and Manabu Okumura. 2016 · 2016
Earlier work this paper cites.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
Earlier work this paper cites.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg. 2017 · 2017
Cited alongside, same era.
Analysing data-to-text generation benchmarks
Laura Perez-Beltrachini and Claire Gardent. 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.
How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C. Lipton. 2018 · 2018
Cited alongside, same era.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson. 2018 · 2018
Cited alongside, same era.
Table-to-text generation by structure-aware seq2seq learning
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui. 2018 · 2018
SHAPED: Shared-private encoder-decoder for text style adaptation
Ye Zhang, Nan Ding, and Radu Soricut. 2018 · 2018
Later among the works it cites.
Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, and William Cohen. 2019 · 2019
Later among the works it cites.
Semantic noise matters for neural natural language generation
Ondřej Dušek, David M. Howcroft, and Verena Rieser. 2019 · 2019
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A simple recipe towards reducing hallucination in neural surface realisation
Feng Nie, Jin-Ge Yao, Jinpeng Wang, Rong Pan, and Chin-Yew Lin. 2019 · 2019
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Leveraging pre-trained checkpoints for sequence generation tasks
Sascha Rothe, Shashi Narayan, and Aliaksei Severyn. 2019 · 2019
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Sticking to the facts: Confident decoding for faithful data-to-text generation
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Cited alongside, same era.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
A structured review of the validity of BLEU
Ehud Reiter. 2018 · 2018
Cited alongside, same era.
Object hallucination in image captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Cited alongside, same era.
Ran Tian, Shashi Narayan, Thibault Sellam, and Ankur P. Parikh. 2019 · 2019
Later among the works it cites.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 2020
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Data-to-text generation with entity modeling
Ratish Puduppully, Li Dong, and Mirella Lapata. 2019 · 2035
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