Vietnamese Poem Generation & The Prospect Of Cross-Language Poem-To-Poem Translation (2401.01078v3)
Abstract: Poetry generation has been a challenging task in the field of Natural Language Processing, as it requires the model to understand the nuances of language, sentiment, and style. In this paper, we propose using LLMs to generate Vietnamese poems of various genres from natural language prompts, thereby facilitating an intuitive process with enhanced content control. Our most efficacious model, the GPT-3 Babbage variant, achieves a custom evaluation score of 0.8, specifically tailored to the "luc bat" genre of Vietnamese poetry. Furthermore, we also explore the idea of paraphrasing poems into normal text prompts and yield a relatively high score of 0.781 in the "luc bat" genre. This experiment presents the potential for cross-Language poem-to-poem translation with translated poems as the inputs while concurrently maintaining complete control over the generated content.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” 2023.
- T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” 2020.
- T. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. Luccioni, F. Yvon, M. Gallé, J. Tow, A. Rush, S. Biderman, A. Webson, P. Ammanamanchi, T. Wang, B. Sagot, N. Muennighoff, A. Moral, and T. Wolf, “Bloom: A 176b-parameter open-access multilingual language model,” 11 2022.
- H. Gonçalo Oliveira, “Poetryme: a versatile platform for poetry generation,” vol. 1, article 21, 08 2012.
- R. Yan, H. Jiang, M. Lapata, s.-d. Lin, X. Lv, and X. Li, “I, poet: Automatic chinese poetry composition through a generative summarization framework under constrained optimization,” 08 2013, pp. 2197–2203.
- L. Jiang and M. Zhou, “Generating chinese couplets using a statistical mt approach.” vol. 1, 01 2008, pp. 377–384.
- Z. Wang, W. He, H. Wu, H. Wu, W. Li, H. Wang, and E. Chen, “Chinese poetry generation with planning based neural network,” 2016.
- Q. Wang, T. Luo, D. Wang, and C. Xing, “Chinese song iambics generation with neural attention-based model,” 2016.
- M. C. Santillan and A. P. Azcarraga, “Poem generation using transformers and doc2vec embeddings,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–7.
- T. Nguyen, H. Pham, T. Bui, T. Nguyen, D. Luong, and P. Nguyen, “Sp-gpt2: Semantics improvement in vietnamese poetry generation,” 2021.
- J. Ye, X. Chen, N. Xu, C. Zu, Z. Shao, S. Liu, Y. Cui, Z. Zhou, C. Gong, Y. Shen, J. Zhou, S. Chen, T. Gui, Q. Zhang, and X. Huang, “A comprehensive capability analysis of gpt-3 and gpt-3.5 series models,” 2023.
- P. Sawicki, M. Grzes, F. Goes, D. Brown, M. Peeperkorn, and A. Khatun, “Bits of grass: Does gpt already know how to write like whitman?” 2023.
- R. Krishnamoorthi, “Quantizing deep convolutional networks for efficient inference: A whitepaper,” 2018.
- E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” 2021.
- J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” 2019.
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