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Generative autoencoders offer a promising approach for controllable text generation by leveraging their latent sentence representations.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Recurrent neural networks are universal approximators
Schäfer, A. M. and Zimmermann, H. G · 2006
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
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Generalized denoising auto-encoders as generative models
Bengio, Y., Yao, L., Alain, G., and Vincent, P · 2013
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Linguistic regularities in continuous space word representations
Mikolov, T., Yih, W.-t., and Zweig, G · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Analyzing noise in autoencoders and deep networks
Poole, B., Sohl-Dickstein, J., and Ganguli, S · 2014
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2015
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Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2016
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Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Toward controlled generation of text
Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., and Xing, E. P · 2017
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Denoising criterion for variational auto-encoding framework
Im, D. I. J., Ahn, S., Memisevic, R., and Bengio, Y · 2017
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Sequence to better sequence: continuous revision of combinatorial structures
Eval all, trust a few, do wrong to none: Comparing sentence generation models
Cífka, O., Severyn, A., Alfonseca, E., and Filippova, K · 2018
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Denoising adversarial autoencoders
Creswell, A. and Bharath, A. A · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Semi-amortized variational autoencoders
Kim, Y., Wiseman, S., Miller, A. C., Sontag, D., and Rush, A. M · 2018
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Content preserving text generation with attribute controls
Logeswaran, L., Lee, H., and Bengio, S · 2018
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Mueller, J., Gifford, D., and Jaakkola, T · 2017
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Style transfer from non-parallel text by cross-alignment
Shen, T., Lei, T., Barzilay, R., and Jaakkola, T · 2017
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Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Improved variational autoencoders for text modeling using dilated convolutions
Yang, Z., Hu, Z., Salakhutdinov, R., and Berg-Kirkpatrick, T · 2017
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
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On the latent space of wasserstein auto-encoders
Rubenstein, P. K., Schoelkopf, B., and Tolstikhin, I · 2018
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Multiple-attribute text style transfer
Subramanian, S., Lample, G., Smith, E. M., Denoyer, L., Ranzato, M., and Boureau, Y.-L · 2018
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Adversarially regularized autoencoders
Zhao, J., Kim, Y., Zhang, K., Rush, A. M., LeCun, Y., et al · 2018
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Lagging inference networks and posterior collapse in variational autoencoders
He, J., Spokoyny, D., Neubig, G., and Berg-Kirkpatrick, T · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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