This paper describes a statistical approach to generation of Chinese classical poetry and proposes a novel method to automatically evaluate poems. The system accepts a set of keywords representing the writing intents from a writer and generates sentences one by one to form a completed poem. A statistical machine translation (SMT) system is applied to generate new sentences, given the sentences generated previously. For each line of sentence a specific model specially trained for that line is used, as opposed to using a single model for all sentences. To enhance the coherence of sentences on every line, a coherence model using mutual information is applied to select candidates with better consistency with previous sentences. In addition, we demonstrate the effectiveness of the BLEU metric for evaluation with a novel method of generating diverse references.
CITATION STYLE
He, J., Zhou, M., & Jiang, L. (2012). Generating Chinese Classical Poems with Statistical Machine Translation Models. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 1650–1656). AAAI Press. https://doi.org/10.1609/aaai.v26i1.8344
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