Abstract
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.
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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.
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Lipa-Urbina, E., Condori-Fernandez, N., Suni-Lopez, F. (2021). Towards an Automatic Generation of Persuasive Messages. In: Ali, R., Lugrin, B., Charles, F. (eds) Persuasive Technology. PERSUASIVE 2021. Lecture Notes in Computer Science(), vol 12684. Springer, Cham. https://doi.org/10.1007/978-3-030-79460-6_5
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DOI: https://doi.org/10.1007/978-3-030-79460-6_5
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