Fetching the paper…
Reading the bibliography…
Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application.
Structural scaffolds for citation intent classification in scientific publications
Arman Cohan, Waleed Ammar, Madeleine van Zuylen, and Field Cady. 2019 · 1904
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2019 · 1904
Earlier work this paper cites.
Easse: Easier automatic sentence simplification evaluation
Fernando Alva-Manchego, Louis Martin, Carolina Scarton, and Lucia Specia. 2019 · 1908
Earlier work this paper cites.
Ctrl: A conditional transformer language model for controllable generation
N. Keskar, B. McCann, L. R. Varshney, Caiming Xiong, and R. Socher. 2019 · 1909
Earlier work this paper cites.
PEGASUS: pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019 · 1912
Earlier work this paper cites.
Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel
J. Peter Kincaid, Robert P Fishburne Jr., Richard L. Rogers, and Brad S. Chissom. 1975 · 1975
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.
TLDR: extreme summarization of scientific documents
Isabel Cachola, Kyle Lo, Arman Cohan, and Daniel S. Weld. 2020 · 2004
Earlier work this paper cites.
Have your text and use it too! end-to-end neural data-to-text generation with semantic fidelity
Hamza Harkous, Isabel Groves, and Amir Saffari. 2020 · 2004
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Fernando Emilio Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, Benoît Sagot, and Lucia Specia. 2020 · 2005
Earlier work this paper cites.
Multilingual unsupervised sentence simplification
Louis Martin, Angela Fan, Éric de la Clergerie, Antoine Bordes, and Benoît Sagot. 2020 · 2005
Earlier work this paper cites.
A study of translation error rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and Ralph Weischedel. 2006 · 2006
Earlier work this paper cites.
Dbpedia: A nucleus for a web of open data
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007 · 2007
Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with high levels of correlation with human judgments
Alon Lavie and Abhaya Agarwal. 2007 · 2007
Earlier work this paper cites.
DART: open-domain structured data record to text generation
Dragomir R. Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Nazneen Fatema Rajani, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Murori Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, and Richard Socher. 2020 · 2007
Earlier work this paper cites.
Visualizing data using t-sne
L. V. D. Maaten and Geoffrey E. Hinton. 2008 · 2008
Earlier work this paper cites.
Gsum: A general framework for guided neural abstractive summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2020 · 2010
Earlier work this paper cites.
Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Luke Zettlemoyer, and Sonal Gupta. 2020 · 2012
Earlier work this paper cites.
Few-shot sequence learning with transformers
Lajanugen Logeswaran, Ann Lee, Myle Ott, Honglak Lee, Marc’Aurelio Ranzato, and Arthur Szlam. 2020 · 2012
Cited alongside, same era.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda B. Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Cited alongside, same era.
Controlling output length in neural encoder-decoders
Yuta Kikuchi, Graham Neubig, Ryohei Sasano, Hiroya Takamura, and Manabu Okumura. 2016 · 2016
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
Empirical study of the benefits of overparameterization in learning latent variable models
Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, and David Sontag. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2020 · 2020
Later among the works it cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
NUIG-DSI at the WebNLG+ challenge: Leveraging transfer learning for RDF-to-text generation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, and Jeff Clune. 2016 · 2016
Cited alongside, same era.
Optimizing statistical machine translation for text simplification
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 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.
Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Cited alongside, same era.
Sentence simplification with deep reinforcement learning
Xingxing Zhang and Mirella Lapata. 2017 · 2017
Cited alongside, same era.
QuickEdit: Editing text & translations by crossing words out
David Grangier and Michael Auli. 2018 · 2018
Cited alongside, same era.
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
Cited alongside, same era.
Nivranshu Pasricha, Mihael Arcan, and Paul Buitelaar. 2020 · 2020
Later among the works it cites.
How fine can fine-tuning be? learning efficient language models
Evani Radiya-Dixit and Xin Wang. 2020 · 2020
Later among the works it 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. 2020 · 2020
Later among the works it cites.
Investigating pretrained language models for graph-to-text generation
Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, and Iryna Gurevych. 2020 · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Later among the works it cites.
The GEM benchmark: Natural language generation, its evaluation and metrics
Sebastian Gehrmann, Tosin P. Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Aremu Anuoluwapo, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna-Adriana Clinciu, Dipanjan Das, Kaustubh D. Dhole, Wanyu Du, Esin Durmus, Ondrej Dusek, Chris Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, Harsh Jhamtani, Yangfeng Ji, Shailza Jolly, Dhruv Kumar, Faisal Ladhak, Aman Madaan, Mounica Maddela, Khyati Mahajan, Saad Mahamood, Bodhisattwa Prasad Majumder, Pedro Henrique Martins, Angelina McMillan-Major, Simon Mille, Emiel van Miltenburg, Moin Nadeem, Shashi Narayan, Vitaly Nikolaev, Rubungo Andre Niyongabo, Salomey Osei, Ankur P. Parikh, Laura Perez-Beltrachini, Niranjan Ramesh Rao, Vikas Raunak, Juan Diego Rodriguez, Sashank Santhanam, João Sedoc, Thibault Sellam, Samira Shaikh, Anastasia Shimorina, Marco Antonio Sobrevilla Cabezudo, Hendrik Strobelt, Nishant Subramani, Wei Xu, Diyi Yang, Akhila Yerukola, and Jiawei Zhou. 2021 · 2021
Closest in time.
Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen. 2021 · 2021
Closest in time.
GENIE: A leaderboard for human-in-the-loop evaluation of text generation
Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, and Daniel S. Weld. 2021 · 2021
Closest in time.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Closest in time.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Closest in time.
Rethinking automatic evaluation in sentence simplification
Thomas Scialom, Louis Martin, Jacopo Staiano, Éric Villemonte de la Clergerie, and Benoît Sagot. 2021 · 2021
Closest in time.
Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, and Felix Hill. 2021 · 2021
Closest in time.
Human evaluation of automatically generated text: Current trends and best practice guidelines
Chris van der Lee, Albert Gatt, Emiel Miltenburg, and Emiel Krahmer. 2021 · 2021
Closest in time.
Attribute alignment: Controlling text generation from pre-trained language models
Dian Yu, Kenji Sagae, and Zhou Yu. 2021 · 2021
Closest in time.