Fetching the paper…
Reading the bibliography…
Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones.
Curriculum learning for domain adaptation in neural machine translation
Xuan Zhang, Pamela Shapiro, Gaurav Kumar, Paul McNamee, Marine Carpuat, and Kevin Duh · 1915
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.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
Paraphrasing for style
Wei Xu, Alan Ritter, William B Dolan, Ralph Grishman, and Colin Cherry. 2012 · 2012
Earlier work this paper cites.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
Earlier work this paper cites.
Curriculum learning and minibatch bucketing in neural machine translation
Tom Kocmi and Ondřej Bojar. 2017 · 2017
Earlier work this paper cites.
Neural question generation from text: A preliminary study
Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou. 2017 · 2017
Cited alongside, same era.
Curriculum learning for natural answer generation
Cao Liu, Shizhu He, Kang Liu, Jun Zhao, et al. 2018 · 2018
Cited alongside, same era.
Does curriculum learning help deep learning for natural language generation?
Sandhya Singh, Kevin Patel, Pushpak Bhattacharyya, Krishnanjan Bhattacharjee, Hemant Darbari, and Seema Verma. 2018 · 2018
Cited alongside, same era.
An empirical exploration of curriculum learning for neural machine translation
Xuan Zhang, Gaurav Kumar, Huda Khayrallah, Kenton Murray, Jeremy Gwinnup, Marianna J Martindale, Paul McNamee, Kevin Duh, and Marine Carpuat. 2018 · 2018
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Cited alongside, same era.
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.
Cdl: Curriculum dual learning for emotion-controllable response generation
Lei Shen and Yang Feng. 2020 · 2020
Later among the works it cites.
Uncertainty-aware curriculum learning for neural machine translation
Yikai Zhou, Baosong Yang, Derek F Wong, Yu Wan, and Lidia S Chao. 2020 · 2020
Later among the works it cites.
Does the order of training samples matter? improving neural data-to-text generation with curriculum learning
Ernie Chang, Hui-Syuan Yeh, and Vera Demberg. 2021 · 2021
Later among the works it cites.
Summeval: Re-evaluating summarization evaluation
Alexander R Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Cited alongside, same era.
Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabás Poczós, and Tom Mitchell. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Dream: A challenge dataset and models for dialogue-based reading comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie. 2019 · 2019
Cited alongside, same era.
Reformulating unsupervised style transfer as paraphrase generation
Kalpesh Krishna, John Wieting, and Mohit Iyyer. 2020 · 2020
Cited alongside, same era.
Token-wise curriculum learning for neural machine translation
Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, and Tuo Zhao. 2021 · 2021
Later among the works it cites.
Competence-based multimodal curriculum learning for medical report generation
Fenglin Liu, Shen Ge, and Xian Wu. 2021 · 2021
Later among the works it cites.
A survey of natural language generation
Chenhe Dong, Yinghui Li, Haifan Gong, Miaoxin Chen, Junxin Li, Ying Shen, and Min Yang. 2022 · 2022
Closest in time.
Let the model decide its curriculum for multitask learning
Neeraj Varshney, Swaroop Mishra, and Chitta Baral. 2022 · 2022
Closest in time.
Reinforcement learning based curriculum optimization for neural machine translation
Gaurav Kumar, George Foster, Colin Cherry, and Maxim Krikun. 2019 · 2061
Closest in time.