The DOI matches its Crossref record; 28 of the 30 references it cites were found in a registry or an index.
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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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Statistical phrase-based translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073462 · Cited by 3,265
Minimum error rate training in statistical machine translation
Och, F. J. (2003) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1075096.1075117 · Cited by 2,728
Extensions of recurrent neural network language model
Mikolov, T., Kombrink, S., Burget, L., Černocký, J., & Khudanpur, S. P. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947611 · Cited by 1,622
Recurrent Continuous Translation Models
Kalchbrenner, N., & Blunsom, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1176 · Cited by 1,277
Semantic Compositionality through Recursive Matrix-Vector Spaces
Socher, R., Huval, B., Manning, C. D., & Ng, A. Y. (2012) · Cited by 1,262
KenLM: Faster and Smaller Language Model Queries
Heafield, K. (2011) · Cited by 1,145
Statistical Phrase-Based Translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · doi:10.21236/ada461156 · Cited by 970
Research works printed but not linked: 6
- Christiane Fellbaum, editor. 1998. WordNet: An Elec- tronic Database. MIT Press, Cambridge, MA.
- Jing He, Ming Zhou, and Long Jiang. 2012. Gener- ating Chinese Classical Poems with Statistical Ma- chine Translation Models. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, pages 1650-1656, Toronto, Canada.
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Cor- rado, and Jeff Dean. 2013. Distributed Representa- tions of Words and Phrases and their Composition- ality. In Advances in Neural Information Process- ing Systems, pages 3111-3119, Lake Tahoe, Nevada, United States.
- Yael Netzer, David Gabay, Yoav Goldberg, and Michael Elhadad. 2009. Gaiku: Generating Haiku with Word Associations Norms. In Proceedings of the Workshop on Computational Approaches to Lin- guistic Creativity, pages 32-39, Boulder, Colorado.
- Naoko Tosa, Hideto Obara, and Michihiko Minoh. 2008. Hitch Haiku: An Interactive Supporting Sys- tem for Composing Haiku Poem How I Learned to Love the Bomb: Defcon and the Ethics of Com- puter Games. In Proceedings of the 7th Inter- national Conference on Entertainment Computing, pages 209-216, Pittsburgh, PA.
- Li Wang. 2002. A Summary of Rhyming Constraints of Chinese Poems (Shi Ci Ge Lv Gai Yao). Beijing Press, 2002.
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Yu, L., Zhang, W., Wang, Y.-X., & Yu, Y. H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10804 · Cited by 2,422
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1082 · Cited by 1,266
Hierarchical Neural Story Generation
Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Wen, T.-H., Gašić, M., Mrkšić, N., Su, P.-H., Vandyke, D., & Young, S. J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1199 · Cited by 974
Neural Summarization by Extracting Sentences and Words
Cheng, J., & Lapata, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1046 · Cited by 865
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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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