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We propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output.
Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein et al. 1966 · 1966
Earlier work this paper cites.
Automated readability index
R. J. Senter and Edgar A Smith. 1967 · 1967
Earlier work this paper cites.
Automatic induction of rules for text simplification
Raman Chandrasekar and Bangalore Srinivas. 1997 · 1997
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Statistical phrase-based post-editing
Michel Simard, Cyril Goutte, and Pierre Isabelle. 2007 · 2007
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Global inference for sentence compression: An integer linear programming approach
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Sentence compression beyond word deletion
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An analysis of statistical models and features for reading difficulty prediction
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Glancing transformer for non-autoregressive neural machine translation
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Earlier work this paper cites.
Machine learning approaches for dealing with limited bilingual training data in statistical machine translation
Gholamreza Haffari. 2009 · 2009
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Efficient reductions for imitation learning
Stephane Ross and Drew Bagnell. 2010 · 2010
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Deep learning for text style transfer: A survey
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A reduction of imitation learning and structured prediction to no-regret online learning
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Imitation learning by coaching
He He, Jason Eisner, and Hal Daume. 2012 · 2012
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, Łukasz Kaiser, and Oriol Vinyals. 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.
Improving multi-step prediction of learned time series models
Arun Venkatraman, Martial Hebert, and J Andrew Bagnell. 2015 · 2015
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Problems in current text simplification research: New data can help
Wei Xu, Chris Callison-Burch, and Courtney Napoles. 2015 · 2015
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A dataset and evaluation metrics for abstractive compression of sentences and short paragraphs
Kristina Toutanova, Chris Brockett, Ke M. Tran, and Saleema Amershi. 2016 · 2016
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Optimizing statistical machine translation for text simplification
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016 · 2016
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Learning how to simplify from explicit labeling of complex-simplified text pairs
Fernando Alva-Manchego, Joachim Bingel, Gustavo Paetzold, Carolina Scarton, and Lucia Specia. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Encode, tag, realize: High-precision text editing
Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. 2019 · 2019
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Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabás Póczos, and Tom M Mitchell. 2019 · 2019
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Curriculum learning in sentiment analysis
Jakub Sido and Miloslav Konopík. 2019 · 2019
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Insertion transformer: Flexible sequence generation via insertion operations
Mitchell Stern, William Chan, Jamie Kiros, and Jakob Uszkoreit. 2019 · 2019
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Simple and effective curriculum pointer-generator networks for reading comprehension over long narratives
Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, and Aston Zhang. 2019 · 2019
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Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K. Li, and Richard Socher. 2018 · 2018
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Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018 · 2018
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Searnn: Training rnns with global-local losses
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, and Simon Lacoste-Julien. 2018 · 2018
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Deterministic non-autoregressive neural sequence modeling by iterative refinement
Jason Lee, Elman Mansimov, and Kyunghyun Cho. 2018 · 2018
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Learning Simplifications for Specific Target Audiences
Carolina Scarton and Lucia Specia. 2018 · 2018
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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
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Controlling text complexity in neural machine translation
Sweta Agrawal and Marine Carpuat. 2019 · 2019
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William Chan, Chitwan Saharia, Geoffrey Hinton, Mohammad Norouzi, and Navdeep Jaitly. 2020 · 2020
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Fine-tuning by curriculum learning for non-autoregressive neural machine translation
Junliang Guo, Xu Tan, Linli Xu, Tao Qin, Enhong Chen, and Tie-Yan Liu. 2020 · 2020
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Norm-based curriculum learning for neural machine translation
Xuebo Liu, Houtim Lai, Derek F Wong, and Lidia S Chao. 2020 · 2020
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FELIX: Flexible text editing through tagging and insertion
Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, and Guillermo Garrido. 2020 · 2020
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Non-autoregressive machine translation with latent alignments
Chitwan Saharia, William Chan, Saurabh Saxena, and Mohammad Norouzi. 2020 · 2020
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Curriculum learning for natural language understanding
Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, and Yongdong Zhang. 2020 · 2020
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Uncertainty-aware curriculum learning for neural machine translation
Yikai Zhou, Baosong Yang, Derek F. Wong, Yu Wan, and Lidia S. Chao. 2020 · 2020
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A non-autoregressive edit-based approach to controllable text simplification
Sweta Agrawal, Weijia Xu, and Marine Carpuat. 2021 · 2021
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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
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Editor: An edit-based transformer with repositioning for neural machine translation with soft lexical constraints
Weijia Xu and Marine Carpuat. 2021 · 2021
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Learning structural edits via incremental tree transformations
Ziyu Yao, Frank F. Xu, Pengcheng Yin, Huan Sun, and Graham Neubig. 2021 · 2021
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Reinforcement learning based curriculum optimization for neural machine translation
Gaurav Kumar, George Foster, Colin Cherry, and Maxim Krikun. 2019 · 2061
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Learning to search better than your teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daume, and John Langford. 2015 · 2066
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