“Vivi-poem-generation”版本间的差异
来自cslt Wiki
| 第39行： | 第39行： | ||
===论文=== | ===论文=== | ||
| − | [http://wangd.cslt.org/public/pdf/aclpoem.pdf Creative generation of poems] | + | * [http://wangd.cslt.org/public/pdf/aclpoem.pdf Creative generation of poems] |
==vivi 1.0== | ==vivi 1.0== | ||
| 第55行： | 第55行： | ||
===测试结果=== | ===测试结果=== | ||
| − | [[中国古诗词图灵测试|vivi 1.0 图灵测试结果]] | + | * [[中国古诗词图灵测试|vivi 1.0 图灵测试结果]] |
===论文=== | ===论文=== | ||
| − | [https://arxiv.org/abs/1604.06274|Chinese Song Iambics Generation with Neural Attention-based Model, IJCAI2016] | + | * [https://arxiv.org/abs/1604.06274|Chinese Song Iambics Generation with Neural Attention-based Model, IJCAI2016] |
| − | [http://link.springer.com/chapter/10.1007/978-3-319-49685-6_4/fulltext.html|Springer, LNCS, vol 10023, pp.171-183.] | + | * [http://link.springer.com/chapter/10.1007/978-3-319-49685-6_4/fulltext.html|Springer, LNCS, vol 10023, pp.171-183.] |
2017年2月12日 (日) 10:27的版本
目录
薇薇：会写诗的机器人
vivi 3.0 (on going)
目标
- Transfer modern sentences to poems
- Utilize extra knowledge to boost innovation
- Reinforcement learning to improve quality
vivi 2.0
基本方法
- Tensorflow 实现
- Attention-based LSTM/GRU S2S
- Sampling words as input to generate the present sentence
- Memory augmentation (global and local)
- Local attention for theme (+)
- Local attention on previous generation, with couplet assignment （line number?） (+)
- N-best decoding (+)
实现细节
- Rythms with less characters removed
- Characters seldom used as rhythms words are removed
- Characters that are low-frequency are removed
特性
- 训练基础模型，用memory实现精细创新
- 用memory可实现风格、体例转换
- 用Local attention可实现人为指导创作（+）
- 可实现律诗中的对仗
测试结果
论文
vivi 1.0
基本方法
- Theano 实现
- 基于sequence-to-sequence的LSTM/GRU模型, 运用Attention 机制。
- 输入为一首诗的第一句，输出为后面所有句子
- 预训练word vectors，用多种体例古文结合在一起训练
- 生成时可对用户输入进行扩展
