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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Chinese Couplet Generation with Neural Network Structures
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Semantic Refinement GRU-Based Neural Language Generation for Spoken Dialogue Systems
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Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)
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Neural Poetry: Learning to Generate Poems Using Syllables
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Multilingual extension and evaluation of a poetry generator
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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