Abstract
In this paper, we investigate techniques that can transfer a news story into a poem. We train cycle-GAN that can conduct text style transfer from news style to poem style even lack of parallel corpus. We compare teacher forcing and free-running modes of training as well as different attention mechanisms in the GAN and cycle-GAN architectures. We found that there is a trade-off between degree of style transfer and content preserving that can be controlled by the ratio of reconstruction and transfer using different training modes of the discriminator and the generator. We show that both GAN and cycle-GAN can be trained to convert news into poems to some extent using non-parallel corpus.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728146669 |
| DOIs | |
| State | Published - 11 2019 |
| Externally published | Yes |
| Event | 24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 - Kaohsiung, Taiwan Duration: 21 11 2019 → 23 11 2019 |
Publication series
| Name | Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 |
|---|
Conference
| Conference | 24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 |
|---|---|
| Country/Territory | Taiwan |
| City | Kaohsiung |
| Period | 21/11/19 → 23/11/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- GAN
- cycle GAN
- deep learning
- poem generation
- text style transfer