Zhonghe Han , Jintao Liu , Yuanben Zhang , Lili Zhang , Lei Wang , Zequn Zhang , Zhihao Zhao , Zhenyu Huang
{"title":"大量引用经典:用历史典故知识提高中国诗歌创作水平","authors":"Zhonghe Han , Jintao Liu , Yuanben Zhang , Lili Zhang , Lei Wang , Zequn Zhang , Zhihao Zhao , Zhenyu Huang","doi":"10.1016/j.csl.2024.101708","DOIUrl":null,"url":null,"abstract":"<div><p>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 <strong>A</strong>llusion-aware <strong>C</strong>hinese <strong>P</strong>oetry <strong>G</strong>eneration (ACPG) framework in this study. Concretely, we first release an <strong>A</strong>llusion-<strong>E</strong>nriched <strong>P</strong>oetry (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.</p></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Copiously Quote Classics: Improving Chinese Poetry Generation with historical allusion knowledge\",\"authors\":\"Zhonghe Han , Jintao Liu , Yuanben Zhang , Lili Zhang , Lei Wang , Zequn Zhang , Zhihao Zhao , Zhenyu Huang\",\"doi\":\"10.1016/j.csl.2024.101708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <strong>A</strong>llusion-aware <strong>C</strong>hinese <strong>P</strong>oetry <strong>G</strong>eneration (ACPG) framework in this study. Concretely, we first release an <strong>A</strong>llusion-<strong>E</strong>nriched <strong>P</strong>oetry (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.</p></div>\",\"PeriodicalId\":50638,\"journal\":{\"name\":\"Computer Speech and Language\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Speech and Language\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0885230824000913\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885230824000913","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.