[1] H. Akaike. Information Theory and an Extension of the Maximum Likelihood Principle, pages 610-624. Springer New York, New York, NY, 1992. ISBN 978-1-4612-0919-5. doi: 10.1007/978-1-4612-0919-5_38. URL https://doi.org/10.1007/978-1-4612-0919-5_38.
[2] D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. In Y. Bengio and Y. LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015. URL http://arxiv.org/abs/1409.0473.
[3] D. Bahdanau, P. Brakel, K. Xu, A. Goyal, R. Lowe, J. Pineau, A. Courville, and Y. Bengio. An actor-critic algorithm for sequence prediction. In International Conference on Learning Representations, 2017. URL https://openreview.net/forum?id=SJDaqqveg.
[4] S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc., 2015. URL https://proceedings.neurips.cc/paper_files/paper/2015/file/e995f98d56967d946471af29d7bf99f1-Paper.pdf.
[5] Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. A neural probabilistic language model. J. Mach. Learn. Res., 3:1137-1155, 2003. URL http://jmlr.org/papers/v3/bengio03a.html.
[6] M. Bhandari, P. N. Gour, A. Ashfaq, P. Liu, and G. Neubig. Re-evaluating evaluation in text summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9347-9359, Online, Nov. 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.emnlp-main.751. URL https://aclanthology.org/2020.emnlp-main.751.
[7] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
[8] K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724-1734, Doha, Qatar, Oct. 2014. Association for Computational Linguistics. doi: 10.3115/v1/D14-1179. URL https://www.aclweb.org/anthology/D14-1179.
[9] A. B. Dieng, K. Cho, D. M. Blei, and Y. LeCun. Learning with reflective likelihoods, 2019. URL https://openreview.net/forum?id=SJ1h2jR9FX.
[10] L. Ding, L. Wang, X. Liu, D. F. Wong, D. Tao, and Z. Tu. Progressive multi-granularity training for non-autoregressive translation. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2797-2803, Online, Aug. 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.findings-acl.247. URL https://aclanthology.org/2021.findings-acl.247.
[11] C. Du, Z. Tu, and J. Jiang. Order-agnostic cross entropy for non-autoregressive machine translation. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 2849-2859. PMLR, 2021. URL http://proceedings.mlr.press/v139/du21c.html.
[12] S. Edunov, M. Ott, M. Auli, D. Grangier, and M. Ranzato. Classical structured prediction losses for sequence to sequence learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 355-364, New Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 10.18653/v1/N18-1033. URL https://aclanthology.org/N18-1033.