SlowGuess/NAACL_2022_OCR huggingface dataset:
Xuan-Phi Nguyen, Shafiq R. Joty, Kui Wu, and Ai Ti Aw. 2020. Data diversification: A simple strategy for neural machine translation. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.
Mohammad Norouzi, Samy Bengio, Zhifeng Chen, Navdeep Jaitly, Mike Schuster, Yonghui Wu, and Dale Schuurmans. 2016. Reward augmented maximum likelihood for neural structured prediction. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 1723-1731.
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. In Proceedings of NAACL-HLT 2019: Demonstrations.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics.
Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, and Lei Li. 2021. Glancing transformer for non-autoregressive neural machine translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1993-2003, Online. Association for Computational Linguistics.
Qiu Ran, Yankai Lin, Peng Li, and Jie Zhou. 2020. Learning to recover from multi-modality errors for non-autoregressive neural machine translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3059-3069, Online. Association for Computational Linguistics.
Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015. Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732.
Aurko Roy, Ashish Vaswani, Arvind Neelakantan, and Niki Parmar. 2018. Theory and experiments on vector quantized autoencoders. arXiv preprint arXiv:1805.11063.
Chitwan Sahara, William Chan, Saurabh Saxena, and Mohammad Norouzi. 2020. Non-autoregressive machine translation with latent alignments. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1098-1108, Online. Association for Computational Linguistics.
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020. BLEURT: Learning robust metrics for text generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7881-7892, Online. Association for Computational Linguistics.
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a. Improving neural machine translation models with monolingual data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 86-96.
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b. Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715-1725, Berlin, Germany. Association for Computational Linguistics.
Yong Shan, Yang Feng, and Chenze Shao. 2021. Modeling coverage for non-autoregressive neural machine translation. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE.
Chenze Shao, Xilin Chen, and Yang Feng. 2018. Greedy search with probabilistic n-gram matching for neural machine translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4778-4784, Brussels, Belgium. Association for Computational Linguistics.
Chenze Shao, Yang Feng, Jinchao Zhang, Fandong Meng, Xilin Chen, and Jie Zhou. 2019. Retrieving sequential information for non-autoregressive neural machine translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3013-3024, Florence, Italy. Association for Computational Linguistics.
Chenze Shao, Yang Feng, Jinchao Zhang, Fandong Meng, and Jie Zhou. 2021. Sequence-Level Training for Non-Autoregressive Neural Machine Translation. Computational Linguistics, 47(4):891-925.
Chenze Shao, Jinchoo Zhang, Yang Feng, Fandong Meng, and Jie Zhou. 2020. Minimizing the bag-of-ngrams difference for non-autoregressive neural machine translation. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, pages 198-205. AAAI Press.
Tianxiao Shen, Myle Ott, Michael Auli, and Marc'Aurelio Ranzato. 2019. Mixture models for diverse machine translation: Tricks of the trade. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 5719-5728. PMLR.
Raphael Shu, Jason Lee, Hideki Nakayama, and Kyunghyun Cho. 2020. Latent-variable non-autoregressive neural machine translation with deterministic inference using a delta posterior. In AAAI.
Jongyoon Song, Sungwon Kim, and Sungroh Yoon. 2021. AligNART: Non-autoregressive neural machine translation by jointly learning to estimate alignment and translate. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1-14, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Zewei Sun, Shujian Huang, Hao-Ran Wei, Xinyu Dai, and Jiajun Chen. 2020. Generating diverse translation by manipulating multi-head attention. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, pages 8976-8983. AAAI Press.
Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, and Zhihong Deng. 2019. Fast structured decoding for sequence models. In Advances in Neural Information Processing Systems 32, pages 3016-3026.
Zhiqing Sun and Yiming Yang. 2020. An EM approach to non-autoregressive conditional sequence generation. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 9249-9258. PMLR.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17, pages 6000-6010, Red Hook, NY, USA. Curran Associates Inc.
Ashwin K. Vijayakumar, Michael Cogswell, Ramprasaath R. Selvaraju, Qing Sun, Stefan Lee, David J. Crandall, and Dhruv Batra. 2018. Diverse beam search for improved description of complex scenes. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 7371-7379. AAAI Press.
Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu. 2019. Non-autoregressive machine translation with auxiliary regularization. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, Hawaii, USA, 2019, pages 5377-5384. AAAI Press.
Bingzhen Wei, Mingxuan Wang, Hao Zhou, Junyang Lin, and Xu Sun. 2019. Imitation learning for non-autoregressive neural machine translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1304-1312, Florence, Italy. Association for Computational Linguistics.
Ronald J. Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn., 8(3-4):229-256.
Ronald J Williams and David Zipser. 1989. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2).
Xuanfu Wu, Yang Feng, and Chenze Shao. 2020. Generating diverse translation from model distribution with dropout. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1088-1097, Online. Association for Computational Linguistics.
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Jiajun Zhang and Chengqing Zong. 2016. Exploiting source-side monolingual data in neural machine translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1535-1545.
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. *Bertscore: Evaluating text generation with bert.* In International Conference on Learning Representations.
Zaixiang Zheng, Hao Zhou, Shujian Huang, Jiajun Chen, Jingjing Xu, and Lei Li. 2021. Duplex sequence-to-sequence learning for reversible machine translation.
Chunting Zhou, Jiatao Gu, and Graham Neubig. 2020. Understanding knowledge distillation in non-autoregressive machine translation. In International Conference on Learning Representations.
Jiawei Zhou and Phillip Keung. 2020. Improving non-autoregressive neural machine translation with monolingual data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1893-1898, Online. Association for Computational Linguistics.