大量引用经典:用历史典故知识提高中国诗歌创作水平

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhonghe Han , Jintao Liu , Yuanben Zhang , Lili Zhang , Lei Wang , Zequn Zhang , Zhihao Zhao , Zhenyu Huang
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引用次数: 0

摘要

将典故融入诗歌是人类诗歌写作的一种高级形式,可以清晰地表达作者的思想,引起读者的共鸣。然而,现有的诗歌生成工作主要集中在提高诗歌的连贯性和流畅性上,而很少考虑生成具有典故知识的诗歌。为了解决这个问题,我们在本研究中提出了一个典故感知中文诗歌创作(ACPG)框架。具体来说,我们首先发布了一个典故丰富诗歌(AEP)数据集,将诗歌与历史典故联系起来,为诗歌生成提供了一个新的研究方向。在此数据集的基础上,我们设计了一种三阶段学习机制,鼓励在低资源环境下的训练阶段,有效利用大规模诗歌和典故数据的知识,生成信息丰富的典故诗。广泛的实验证明了 ACPG 在一系列拟议基线中的有效性。此外,所提出的 ACPG 框架还可应用于歌词生成或其他受控文本生成任务,从而将典故知识纳入生成结果,增强文本的意义和质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Copiously Quote Classics: Improving Chinese Poetry Generation with historical allusion knowledge

Integrating allusions into poems is an advanced form of human poetry writing, which could clearly express the author’s thoughts and arouse the resonance of readers. However, existing poetry generation works mainly focus on improving the coherence and fluency of poetry, while generating poems with allusion knowledge is rarely considered. To solve this issue, we propose an Allusion-aware Chinese Poetry Generation (ACPG) framework in this study. Concretely, we first release an Allusion-Enriched Poetry (AEP) dataset by linking poems with historical allusions, which might enable a new research direction for poetry generation. Based on this dataset, we design a three-stage learning mechanism to encourage the training stage under a low-resource setting, which can effectively exploit the knowledge of large-scale poetry and allusion data to generate informative allusive poems. Extensive experiments demonstrate the effectiveness of ACPG among a series of proposed baselines. Moreover, the proposed ACPG framework can also be applied to lyrics generation or other controlled text generation tasks, which can incorporate allusion knowledge into the generated results and enhance the meaning and quality of the texts.

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来源期刊
Computer Speech and Language
Computer Speech and Language 工程技术-计算机:人工智能
CiteScore
11.30
自引率
4.70%
发文量
80
审稿时长
22.9 weeks
期刊介绍: Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language. The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.
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