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Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation.
Answers unite! unsupervised metrics for reinforced summarization models
Scialom, T., Lamprier, S., Piwowarski, B., and Staiano, J · 1909
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A learning algorithm for continually running fully recurrent neural networks
Williams, R. J. and Zipser, D · 1989
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Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Discriminative adversarial search for abstractive summarization
Scialom, T., Dray, P.-A., Lamprier, S., Piwowarski, B., and Staiano, J · 2002
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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Modeling purposeful adaptive behavior with the principle of maximum causal entropy
Ziebart, B. D · 2010
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Multi-armed bandits with episode context
Rosin, C. D · 2011
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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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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
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Sequence level training with recurrent neural networks
Ranzato, M., Chopra, S., Auli, M., and Zaremba, W · 2015
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B., et al · 2016
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Reward augmented maximum likelihood for neural structured prediction
Norouzi, M., Bengio, S., Jaitly, N., Schuster, M., Wu, Y., Schuurmans, D., et al · 2016
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Thinking fast and slow with deep learning and tree search
Anthony, T., Tian, Z., and Barber, D · 2017
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Maximum-likelihood augmented discrete generative adversarial networks
Che, T., Li, Y., Zhang, R., Hjelm, R. D., Li, W., Song, Y., and Bengio, Y · 2017
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A deep reinforced model for abstractive summarization
Paulus, R., Xiong, C., and Socher, R · 2017
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Cot: Cooperative training for generative modeling of discrete data
Lu, S., Yu, L., Feng, S., Zhu, Y., and Zhang, W · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2019
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Neural text generation with unlikelihood training
Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., and Weston, J · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2019
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Language gans falling short
Caccia, M., Caccia, L., Fedus, W., Larochelle, H., Pineau, J., and Charlin, L · 2020
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Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Yu, L., Zhang, W., Wang, J., and Yu, Y · 2017
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Long text generation via adversarial training with leaked information
Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., and Wang, J · 2018
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Ma, Z. and Collins, M · 2018
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Learning beam search policies via imitation learning
Negrinho, R., Gormley, M., and Gordon, G. J · 2018
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Training language gans from scratch
de Masson d’Autume, C., Mohamed, S., Rosca, M., and Rae, J · 2019
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2019
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Residual energy-based models for text generation
Deng, Y., Bakhtin, A., Ott, M., Szlam, A., and Ranzato, M · 2020
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Improving maximum likelihood training for text generation with density ratio estimation
Song, Y., Miao, N., Zhou, H., Yu, L., Wang, M., and Li, L · 2020
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Residual energy-based models for text
Bakhtin, A., Deng, Y., Gross, S., Ott, M., Ranzato, M., and Szlam, A · 2021
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PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided Decoding
Chaffin, A., Claveau, V., and Kijak, E · 2021
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Machine translation decoding beyond beam search
Leblond, R., Alayrac, J.-B., Sifre, L., Pislar, M., Lespiau, J.-B., Antonoglou, I., Simonyan, K., and Vinyals, O · 2021
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To beam or not to beam: That is a question of cooperation for language gans
Scialom, T., Dray, P., Lamprier, S., Piwowarski, B., and Staiano, J · 2021
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