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Chinese Poetry Generation with Recurrent Neural Networks
Open accessAbstract
We propose a model for Chinese poem generation based on recurrent neural networks which we argue is ideally suited to capturing poetic content and form.Our generator jointly performs content selection ("what to say") and surface realization ("how to say") by learning representations of individual characters, and their combinations into one or more lines as well as how these mutually reinforce and constrain each other.Poem lines are generated incrementally by taking into account the entire history of what has been generated so far rather than the limited horizon imposed by the previous line or lexical n-grams.Experimental results show that our model outperforms competitive Chinese poetry generation systems using both automatic and manual evaluation methods.
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Metadata
- Authors
- Zhang, X., & Lapata, M.
- Affiliations
- University of Edinburgh (GB)
- Venue
- Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2014
- Pages
- 670-680
- OpenAlex
- W2115221470
- Cited by
- 298
- References
- 30
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 13 · 2023: 20 · 2022: 18 · 2021: 39 · 2020: 46 · 2019: 50 · 2018: 45 · 2017: 31 · 2016: 14 · 2015: 10
- FWCI
- 10.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- recurrent neural networks, content selection, surface realization, character representation, long-range dependencies, poetic form
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