Generating Better Responses from User Feedback via Reinforcement Learning and Commonsense Inference
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44699-3_34
Reference16 articles.
1. Jaques, N., et al.: Human-centric dialog training via offline reinforcement learning. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16–20, 2020, pp. 3985–4003 (2020)
2. Gao, X., Zhang, Y., Galley, M., Brockett, C., Dolan, B.: Dialogue response ranking training with large-scale human feedback data. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16–20, 2020, pp. 386–395 (2020)
3. Zhang, S., et al.: Multi-action dialog policy learning from logged user feedback. arXiv preprint arXiv:2302.13505 (2023)
4. Ziegler, D.M., et al.: Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019)
5. Wu, Z., Bi, W., Li, X., Kong, L., Kao, B.: Lexical knowledge internalization for neural dialog generation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22–27, 2022, pp. 7945–7958 (2022)
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