5th International Conference on Computational Creativity (ICCC 2014) Ljubljana, Slovenia, 2014., June 2014
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Year 2019 : 2 citations
Son, S.-H., Lee, H.-Y., Nam, G.-H., and Kang, S.-S. (2019). Korean song-lyrics generation by deep learning. In Proceedings of the 4th International Conference on Intelligent Information Technology, ICIIT ’19, pages 96–100. ACM.
Zhang, R., Liu, X., Chen, X., Hu, Z., Xu, Z., and Mao, Y. (2019). Generating Chinese Ci with designated metrical structure. In Proceedings of 33rd AAAI Conference on Artificial Intelligence, pages 7459–7467. AAAI Press.
Year 2017 : 3 citations
Tikhonov, A. and Yamshchikov, I. P. (2017). Music generation with variational recurrent autoencoder supported by history. In Proceedings of the 13th International Sympo- sium on Computer Music Multidisciplinary Research, Matosinhos, Portugal, Sept. 25-28, 2017, CMMR 2017, pages 527–537.
Agirrezabal, M., Alegria, I., and Hulden, M. (2017). A comparison of feature-based and neural scansion of poetry. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 18–23, Varna, Bulgaria. INCOMA Ltd.
Astigarraga, A., María Martínez-Otzeta, J., Rodriguez Rodriguez, I., Sierra, B., and Lazkano, E. (2017). Poet’s little helper: A methodology for computer-based poetry generation. a case study for the basque language. In Proceedings of the Workshop on Computational Creativity in Natural Language Generation (CC-NLG 2017), pages 2–10, Santiago de Compostela, Spain. Association for Computational Linguistics.
Year 2016 : 1 citations
Wang, Z., He, W., Wu, H., Wu, H., Li, W., Wang, H., and Chen, E. (2016). Chinese poetry generation with planning based neural network. CoRR, abs/1610.09889.
Year 2015 : 4 citations
Hayes, M. D. and Wiggins, G. A. (2015). Adding semantics to statistical generation for poetic creativity. In Late-breaking abstract of the 6th International Conference on Computational Creativity, ICCC 2015.
Potash, P., Romanov, A., and Rumshisky, A. (2015). Ghostwriter: Using an lstm for automatic rap lyric generation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, pages 1919–1924, Lisbon, Portugal. ACL Press.
Toivonen, H. and Gross, O. (2015). Data mining and machine learning in computational creativity. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery archive. Volume 5 Issue 6, November 2015. Pages 265-275.
Scirea, M., Barros, G. A. B., Shaker, N., and Togelius, J. (2015). Smug: Scientific music generator. In Proceedings of the 6th International Conference on Computational Creativity, ICCC 2015, pages 204–211, Park City, UT, USA.
