[55] Seonggi Ryang and Takeshi Abekawa. Framework of automatic text summarization using reinforcement learning. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 256-265, Jeju Island, Korea, July 2012. Association for Computational Linguistics. URL https://aclanthology.org/D12-1024.
[56] Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks. arXiv preprint arXiv:1511.06732, 2015.
[57] Romain Paulus, Caiming Xiong, and Richard Socher. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017.
[58] Raphael Shu, Kang Min Yoo, and Jung-Woo Ha. Reward optimization for neural machine translation with learned metrics. arXiv preprint arXiv:2104.07541, 2021.
[59] Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, and Yejin Choi. Quark: Controllable text generation with reinforced unlearning. arXiv preprint arXiv:2205.13636, 2022.
[60] Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P Xing, and Zhiting Hu. Rlprompt: Optimizing discrete text prompts with reinforcement learning. arXiv preprint arXiv:2205.12548, 2022.
[61] Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, and Yejin Choi. Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv preprint arXiv:2210.01241, 2022.
[62] Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Å. Lapedriza, Noah J. Jones, S. Gu, and Rosalind W. Picard. Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog. ArXiv, abs/1907.00456, 2019.
[63] Natasha Jaques, Judy Hanwen Shen, Asma Ghandehariousn, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, and Rosalind Picard. Human-centric dialog training via offline reinforcement learning. arXiv preprint arXiv:2010.05848, 2020.
[64] Shujian Zhang, Chengyue Gong, and Xingchao Liu. Passage-mask: A learnable regularization strategy for retriever-reader models. arXiv preprint arXiv:2211.00915, 2022.
[65] Louis Castricato, Alexander Havrilla, Shahbuland Matiana, Michael Pieler, Anbang Ye, Ian Yang, Spencer Frazier, and Mark Riedl. Robust preference learning for storytelling via contrastive reinforcement learning. arXiv preprint arXiv:2210.07792, 2022.
[66] Zhan Shi, Xinchi Chen, Xipeng Qiu, and Xuanjing Huang. Toward diverse text generation with inverse reinforcement learning. arXiv preprint arXiv:1804.11258, 2018.
[67] Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang. Long text generation via adversarial training with leaked information. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
[68] Zichao Yang, Zhiting Hu, Chris Dyer, Eric P Xing, and Taylor Berg-Kirkpatrick. Unsupervised text style transfer using language models as discriminators. Advances in Neural Information Processing Systems, 31, 2018.
[69] Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun. Adversarial ranking for language generation. Advances in neural information processing systems, 30, 2017.
[70] Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the AAAI conference on artificial intelligence, volume 31, 2017.
[71] Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. Advances in Neural Information Processing Systems, 35:21314-21328, 2022.