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Text autoencoders are commonly used for conditional generation tasks such as style transfer.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 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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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010 · 2010
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Normalized word embedding and orthogonal transform for bilingual word translation
Chao Xing, Dong Wang, Chao Liu, and Yiye Lin. 2015 · 2015
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence RNNs and beyond
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Çağlar Gu̇lçehre, and Bing Xiang. 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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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
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Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017 · 2017
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Exploring neural text simplification models
Sergiu Nisioi, Sanja Štajner, Simone Paolo Ponzetto, and Liviu P. Dinu. 2017 · 2017
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A hybrid convolutional variational autoencoder for text generation
Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth. 2017 · 2017
Cited alongside, same era.
Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Sentence simplification with deep reinforcement learning
Xingxing Zhang and Mirella Lapata. 2017 · 2017
Cited alongside, same era.
Unsupervised neural machine translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. 2018 · 2018
Cited alongside, same era.
Eval all, trust a few, do wrong to none: Comparing sentence generation models
Salsa-text: self attentive latent space based adversarial text generation
Jules Gagnon-Marchand, Hamed Sadeghi, Md Akmal Haidar, and Mehdi Rezagholizadeh. 2019 · 2019
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IMaT: Unsupervised text attribute transfer via iterative matching and translation
Zhijing Jin, Di Jin, Jonas Mueller, Nicholas Matthews, and Enrico Santus. 2019 · 2019
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Learning to flip the sentiment of reviews from non-parallel corpora
Canasai Kruengkrai. 2019 · 2019
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Von Mises-Fisher loss for training sequence to sequence models with continuous outputs
Sachin Kumar and Yulia Tsvetkov. 2019 · 2019
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Multiple-attribute text rewriting
Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc’Aurelio Ranzato, and Y-Lan Boureau. 2019 · 2019
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Domain adaptive text style transfer
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Ondřej Cífka, Aliaksei Severyn, Enrique Alfonseca, and Katja Filippova. 2018 · 2018
Cited alongside, same era.
Latent constraints: Learning to generate conditionally from unconditional generative models
Jesse Engel, Matthew Hoffman, and Adam Roberts. 2018 · 2018
Cited alongside, same era.
Unsupervised sentence compression using denoising auto-encoders
Thibault Fevry and Jason Phang. 2018 · 2018
Cited alongside, same era.
Style transfer in text: Exploration and evaluation
Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, and Rui Yan. 2018 · 2018
Cited alongside, same era.
Delete, retrieve, generate: a simple approach to sentiment and style transfer
Juncen Li, Robin Jia, He He, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Content preserving text generation with attribute controls
Lajanugen Logeswaran, Honglak Lee, and Samy Bengio. 2018 · 2018
Cited alongside, same era.
Simplifying sentences with sequence to sequence models
Alexander Mathews, Lexing Xie, and Xuming He. 2018 · 2018
Cited alongside, same era.
Dianqi Li, Yizhe Zhang, Zhe Gan, Yu Cheng, Chris Brockett, Bill Dolan, and Ming-Ting Sun. 2019 · 2019
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A transformer-based variational autoencoder for sentence generation
Danyang Liu and Gongshen Liu. 2019 · 2019
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To tune or not to tune? Adapting pretrained representations to diverse tasks
Matthew E. Peters, Sebastian Ruder, and Noah A. Smith. 2019 · 2019
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Towards lossless encoding of sentences
Gabriele Prato, Mathieu Duchesneau, Sarath Chandar, and Alain Tapp. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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MASS: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
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“Transforming” delete, retrieve, generate approach for controlled text style transfer
Akhilesh Sudhakar, Bhargav Upadhyay, and Arjun Maheswaran. 2019 · 2019
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Unsupervised pretraining for neural machine translation using elastic weight consolidation
Dušan Variš and Ondřej Bojar. 2019 · 2019
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Controllable unsupervised text attribute transfer via editing entangled latent representation
Ke Wang, Hang Hua, and Xiaojun Wan. 2019 · 2019
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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
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Pre-train and plug-in: Flexible conditional text generation with variational auto-encoders
Yu Duan, Canwen Xu, Jiaxin Pei, Jialong Han, and Chenliang Li. 2020 · 2020
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From variational to deterministic autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf. 2020 · 2020
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Revision in continuous space: Unsupervised text style transfer without adversarial learning
Dayiheng Liu, Jie Fu, Yidan Zhang, Chris Pal, and Jiancheng Lv. 2020 · 2020
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Educating text autoencoders: Latent representation guidance via denoising
Tianxiao Shen, Jonas Mueller, Regina Barzilay, and Tommi Jaakkola. 2020 · 2020
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