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        <title>Poetry Generation Combining Poetry Theme Labels Representations</title>
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        <namePart type="given">Yingyu</namePart>
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        <namePart type="given">Dongzhen</namePart>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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    <abstract>Ancient Chinese poetry is the earliest literary genre that took shape in Chinese literature and has a dissemination effect, showing China’s profound cultural heritage. At the same time, the generation of ancient poetry is an important task in the field of digital humanities, which is of great significance to the inheritance of national culture and the education of ancient poetry. The current work in the field of poetry generation is mainly aimed at improving the fluency and structural accuracy of words and sentences, ignoring the theme unity of poetry generation results. In order to solve this problem, this paper proposes a graph neural network poetry theme representation model based on label embedding. On the basis of the network representation of poetry, the topic feature representation of poetry is constructed and learned from the granularity of words. Then, the features of the poetry theme representation model are combined with the autoregressive language model to construct a theme-oriented ancient Chinese poetry generation model TLPG (Poetry Generation with Theme Label). Through machine evaluation and evaluation by experts in related fields, the model proposed in this paper has significantly improved the topic consistency of poetry generation compared with existing work on the premise of ensuring the fluency and format accuracy of poetry.</abstract>
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%0 Conference Proceedings
%T Poetry Generation Combining Poetry Theme Labels Representations
%A Yan, Yingyu
%A Wen, Dongzhen
%A Yang, Liang
%A Zhang, Dongyu
%A Lin, Hongfei
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F yan-etal-2023-poetry
%X Ancient Chinese poetry is the earliest literary genre that took shape in Chinese literature and has a dissemination effect, showing China’s profound cultural heritage. At the same time, the generation of ancient poetry is an important task in the field of digital humanities, which is of great significance to the inheritance of national culture and the education of ancient poetry. The current work in the field of poetry generation is mainly aimed at improving the fluency and structural accuracy of words and sentences, ignoring the theme unity of poetry generation results. In order to solve this problem, this paper proposes a graph neural network poetry theme representation model based on label embedding. On the basis of the network representation of poetry, the topic feature representation of poetry is constructed and learned from the granularity of words. Then, the features of the poetry theme representation model are combined with the autoregressive language model to construct a theme-oriented ancient Chinese poetry generation model TLPG (Poetry Generation with Theme Label). Through machine evaluation and evaluation by experts in related fields, the model proposed in this paper has significantly improved the topic consistency of poetry generation compared with existing work on the premise of ensuring the fluency and format accuracy of poetry.
%U https://aclanthology.org/2023.ranlp-1.132
%P 1246-1255

Markdown (Informal)

[Poetry Generation Combining Poetry Theme Labels Representations](https://aclanthology.org/2023.ranlp-1.132) (Yan et al., RANLP 2023)

• Poetry Generation Combining Poetry Theme Labels Representations (Yan et al., RANLP 2023)

ACL

• Yingyu Yan, Dongzhen Wen, Liang Yang, Dongyu Zhang, and Hongfei Lin. 2023. Poetry Generation Combining Poetry Theme Labels Representations. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1246–1255, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.