“第二十九章 AI诗人”版本间的差异
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− | *拼凑法 | + | *拼凑法: 一种宋词自动生成的遗传算法及其机器实现:[http://www.jos.org.cn/jos/article/pdf/3596?st=article_issue] |
− | * | + | *概率法: Generating Chinese couplets using a statistical MT approach:[https://aclanthology.org/C08-1048.pdf] |
− | + | *神经网络法: Chinese poetry generation with recurrent neural networks:[https://aclanthology.org/D14-1074.pdf] | |
− | + | *宋词的神经网络生成: Chinese song iambics generation with neural attention-based model[https://arxiv.org/pdf/1604.06274.pdf] | |
− | + | *灵活创新性诗歌生成:Flexible and Creative Chinese Poetry Generation Using Neural Memory[https://arxiv.org/pdf/1705.03773.pdf] | |
− | + | *清华大学CSLT AI诗人薇薇:Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test[https://arxiv.org/pdf/1606.05829.pdf] | |
− | * | + | *图片流作诗:Images2Poem : Generating Chinese Poetry from Image Streams[https://dl.acm.org/doi/pdf/10.1145/3240508.3241910 ] |
2023年8月13日 (日) 02:18的最后版本
教学资料
扩展阅读
- 百度百科:诗歌 [1]
- 百度百科:绝句律诗的格律 [2]
- AI100问:AI如何成为诗人 [3]
- 以画入诗原文:How images inspire poems: Generating classical chinese poetry from images with memory networks
- 百度百科:诗学含英 [4]
- 《笠翁对韵》全文阅读 [5]
演示链接
开发者资源
- 中国诗歌数据库 [9]
- Pytorch book中一个简单的RNN-CHAR生成古诗的github代码库 [10]
- 一个基于Keras的notebook程序,应用起来更简单[11]
- 用UER[12]训练出的transformer模型,转成hugging face transformers模式[13],可直接用预训练模型测试性能。注意,需要装transformers。实测TF模型可用,pytorch模型有问题。[14]
高级读者
- 拼凑法: 一种宋词自动生成的遗传算法及其机器实现:[15]
- 概率法: Generating Chinese couplets using a statistical MT approach:[16]
- 神经网络法: Chinese poetry generation with recurrent neural networks:[17]
- 宋词的神经网络生成: Chinese song iambics generation with neural attention-based model[18]
- 灵活创新性诗歌生成:Flexible and Creative Chinese Poetry Generation Using Neural Memory[19]
- 清华大学CSLT AI诗人薇薇:Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test[20]
- 图片流作诗:Images2Poem : Generating Chinese Poetry from Image Streams[21]