Machine Masquerades a Poet: Using Unsupervised T5 Transformer for Semantic Style Transformation in Poetry Generation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46813-1_27
Reference11 articles.
1. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1–67 (2020). arXiv:1910.10683
2. Ahmad, S., Asghar, M.Z., Alotaibi, F.M., Khan, S.: Classification of poetry text into the emotional states using deep learning technique. IEEE Access 8, 73865–73878 (2020). https://doi.org/10.1109/ACCESS.2020.2987842
3. Wei, J., Zhou, Q., Cai, Y.: Poet-based poetry generation: controlling personal style with recurrent neural networks. In: 2018 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA, pp. 156–160 (2018). https://doi.org/10.1109/ICCNC.2018.8390270
4. Talafha, S., Rekabdar, B.: Poetry generation model via deep learning incorporating extended phonetic and semantic embeddings. In: 2021 IEEE 15th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, pp. 48–55 (2021). https://doi.org/10.1109/ICSC50631.2021.00013
5. Fu, Z., Tan, X., Peng, N., Zhao, D., Yan, R.: Style transfer in text: exploration and evaluation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018). https://doi.org/10.1609/aaai.v32i1.11330
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