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Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents.
CTRL: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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Self-adversarial learning with comparative discrimination for text generation
Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, and Ming Zhou. 2020 · 1911
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng. 2013 · 2013
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Semi-supervised learning with deep generative models
Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014 · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling. 2014 · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero. 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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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015 · 2015
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016 · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
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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.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
Cited alongside, same era.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg. 2017 · 2017
Cited alongside, same era.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
Cited alongside, same era.
Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi S. 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, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Question generation from SQL queries improves neural semantic parsing
Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, and Ming Zhou. 2018 · 2018
Later among the works it cites.
Disentangling by factorising
Hyunjik Kim and Andriy Mnih. 2018 · 2018
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Polite dialogue generation without parallel data
Tong Niu and Mohit Bansal. 2018 · 2018
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Wasserstein auto-encoders
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schölkopf. 2018 · 2018
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Sentigan: Generating sentimental texts via mixture adversarial networks
Ke Wang and Xiaojun Wan. 2018 · 2018
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Wasserstein divergence for gans
Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, and Luc Van Gool. 2018 · 2018
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Liwei Wang, Alexander G. Schwing, and Svetlana Lazebnik. 2017 · 2017
Cited alongside, same era.
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017 · 2017
Cited alongside, same era.
Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
Tiancheng Zhao, Ran Zhao, and Maxine Eskénazi. 2017 · 2017
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger B. Grosse, and David K. Duvenaud. 2018 · 2018
Cited alongside, same era.
Learning disentangled joint continuous and discrete representations
Emilien Dupont. 2018 · 2018
Cited alongside, same era.
Latent constraints: Learning to generate conditionally from unconditional generative models
Jesse H. Engel, Matthew Hoffman, and Adam Roberts. 2018 · 2018
Cited alongside, same era.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Cited alongside, same era.
Generating thematic chinese poetry using conditional variational autoencoders with hybrid decoders
Xiaopeng Yang, Xiaowen Lin, Shunda Suo, and Ming Li. 2018 · 2018
Later among the works it cites.
Chinese poetry generation with a working memory model
Xiaoyuan Yi, Maosong Sun, Ruoyu Li, and Zonghan Yang. 2018 · 2018
Later among the works it cites.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019 · 2019
Closest in time.
Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzkebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
Ali Razavi, Aäron van den Oord, and Oriol Vinyals. 2019 · 2019
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What makes a good conversation? how controllable attributes affect human judgments
Abigail See, Stephen Roller, Douwe Kiela, and Jason Weston. 2019 · 2019
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