Abstract
As one of the quintessence of Chinese traditional culture, couplet compromises two syntactically symmetric clauses equal in length, namely, an antecedent and subsequent clause. Moreover, corresponding characters and phrases at the same position of the two clauses are paired with each other under certain constraints of semantic and/or syntactic relatedness. Automatic couplet generation is recognized as a challenging problem even in the Artificial Intelligence field. In this paper, we comprehensively study on automatic generation of acrostic couplet with the first characters defined by users. The complete couplet generation is mainly divided into three stages, that is, antecedent clause generation pipeline, subsequent clause generation pipeline and clause re-ranker. To realize semantic and/or syntactic relatedness between two clauses, attention-based Sequence-to-Sequence (S2S) neural network is employed. Moreover, to provide diverse couplet candidates for re-ranking, a cluster-based beam search approach is incorporated into the S2S network. Both BLEU metrics and human judgments have demonstrated the effectiveness of our proposed method. Eventually, a mini-program based on this generation system is developed and deployed on Wechat for real users.
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References
Mann, W.: Text generation. Comput. Linguist. 8, 62–69 (1982)
Zhang, K., Sun, M.: An Chinese couplet generation model based on statistics and rules. J. Chin. Inf. Process. 23(1), 100–105 (2009)
Jiang, L., Zhou, S.: Generating Chinese couplets using a statistical MT approach. In: International Conference on Computational Linguistics, vol. 21, no. 3, pp. 427–437 (2008)
Yan, R., Li, C., Hu, X.: Chinese couplet generation with neural network structures. In: Association for Computer Linguistics, Berlin, pp. 2347–2357 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations (2015)
Wang, Z., He, W., Wu, H.: Chinese poetry generation with planning based neural network. In: International Conference on Computational Linguistics, pp. 1051–1060 (2016)
Zhang, J., Feng, Y., Wang, D.: Flexible and creative Chinese poetry generation using neural memory. In: Computing Research Repository (2017)
Bengio, Y., Ducharme, R., Vincent, P.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Mikolov, T.: Recurrent neural network based language model. Interspeech 2, 3 (2010)
Sutskever, I., Vinyals, O., Le, Q.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 4, pp. 3104–3112 (2014)
Mikolov, T., Sutskever, I., Chen, K.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119 (2013)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, San Diego (2015)
Chowdhury, G.G.: Natural language processing. In: Annual Review of Information Science and Technology, pp. 51–89 (2003)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences (2014). arXiv preprint arXiv:1404.2188
Hochreither, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Computer Science (2014)
Koehn, P.: Statistical Machine Translation. Cambridge University Press, Cambridge (2010)
Koehn, P.: Neural machine translation. Comput. Lang. (2017). arXiv:1709.07809
Koehn, P., Och, F.J., Marcu, D., et al.: Statistical phrase-based translation. In: North American Chapter of the Association for Computational Linguistics, pp. 48–54 (2003)
Devlin, J., Zbib, R., Huang, Z., et al.: Fast and robust neural network joint models for statistical machine translation. In: Meeting of the Association for Computational Linguistics, pp. 1370–1380 (2014)
Ahmed, K., Keskar, N.S., Socher, R., et al.: Weighted transformer network for machine translation. Artif. Intell. (2018). arXiv:1711.02132
Wu, X., Tosa, N., Nakatsu, R.: New Hitch Haiku: an interactive Renku poem composition supporting tool applied for sightseeing navigation system. In: Natkin, S., Dupire, J. (eds.) ICEC 2009. LNCS, vol. 5709, pp. 191–196. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04052-8_19
Oliveira, H.G.: PoeTryMe: a versatile platform for poetry generation. In: Computational Creativity, Concept Invention, and General Intelligence, vol. 1, p. 21 (2012)
Yan, R., Jiang, H., Lapata, M., Lin, S.D., Lv, X., Li, X.: i, Poet: automatic Chinese poetry composition through a generative summarization framework under constrained optimization. In: International Joint Conference on Artificial Intelligence, pp. 2197–2203 (2013)
Zhang, X., Lapata, M.: Chinese poetry generation with recurrent neural networks. In: Empirical Methods in Natural Language Processing, pp. 670–680 (2014)
Sordoni, A., Bengio, Y., Vahabi, H., Lioma, C., Simonsen, J.G., Nie, J.Y.: A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In: Computing Research Repository, pp. 553–562 (2015)
Tam, Y., Ding, J., Niu, C., Zhou, J.: Cluster-based beam search for pointer-generator chatbot grounded by knowledge. In: Proceedings of the Thirty-Three AAAI Conference on Artificial Intelligence (AAAI 2019) (2019)
Papineni, K.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL) (2002)
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This work was supported by Ping An Technology (Shenzhen) Co., Ltd, China.
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Fan, H., Wang, J., Zhuang, B., Wang, S., Xiao, J. (2019). Automatic Acrostic Couplet Generation with Three-Stage Neural Network Pipelines. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_25
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