In the last decades, the Natural Language Generation (NLG) methods have been improved to generate text automatically. However, based on the literature review, there are not works on generating text for persuading people. In this paper, we propose to use the SentiGAN framework to generate messages that are classified into levels of persuasiveness. And, we run an experiment using the Microtext dataset for the training phase. Our preliminary results show 0.78 of novelty on average, and 0.57 of diversity in the generated messages.
|Title of host publication||Persuasive Technology - 16th International Conference, PERSUASIVE 2021, Proceedings|
|Editors||Raian Ali, Birgit Lugrin, Fred Charles|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||8|
|State||Published - 2021|
|Event||16th International Conference on Persuasive Technology, PERSUASIVE 2021 - Virtual, Online|
Duration: 12 Apr 2021 → 14 Apr 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||16th International Conference on Persuasive Technology, PERSUASIVE 2021|
|Period||12/04/21 → 14/04/21|
Bibliographical noteFunding Information:
Acknowledgments. This work has been supported by CONCYTEC - FONDECYT within the framework of the call E038-01 contract 014-2019. N. Condori Fernandez wish also to thank Datos 4.0 (TIN2016-78011-C4-1-R) funded by MINECO-AEI/FEDER-UE.
This work has been supported by CONCYTEC-FONDECYT within the framework of the call E038-01 contract 014-2019. N. Condori Fernandez wish also to thank Datos 4.0 (TIN2016-78011-C4-1-R) funded by MINECO-AEI/FEDER-UE.
© 2021, Springer Nature Switzerland AG.
- Persuasive message
- Text generation