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Authors: Andres Pautrat-Lertora ; Renzo Perez-Lozano and Willy Ugarte

Affiliation: Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru

Keyword(s): GAN, Text Generation, NAO.

Abstract: In these last years, communication with computers has made enormous steps, like the robot Sophia that surprised many people with their human interactions, behind this kind of robot, there is a machine learning model for text generation to interact with others, but in terms of text generation with sentiments not many investigations have been done. A model like GAN has opportunities to become an excellent option to attack this new problem because of their discriminator and generator competing for search the optimal solution. In this paper, a GAN model is presented that can generate text with different emotions based on a dataset recompiled from tweets labeled with emotions and then deployed in an NAO robot to speak the text in short phrases using voice commands. The model is evaluated with different methods popular in text generation like BLLEU and additionally, a human experiment is done to prove the quality and sentiment accuracy.

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Paper citation in several formats:
Pautrat-Lertora, A.; Perez-Lozano, R. and Ugarte, W. (2022). EGAN: Generatives Adversarial Networks for Text Generation with Sentiments. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR; ISBN 978-989-758-614-9; ISSN 2184-3228, SciTePress, pages 249-256. DOI: 10.5220/0011548100003335

@conference{kdir22,
author={Andres Pautrat{-}Lertora. and Renzo Perez{-}Lozano. and Willy Ugarte.},
title={EGAN: Generatives Adversarial Networks for Text Generation with Sentiments},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR},
year={2022},
pages={249-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011548100003335},
isbn={978-989-758-614-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR
TI - EGAN: Generatives Adversarial Networks for Text Generation with Sentiments
SN - 978-989-758-614-9
IS - 2184-3228
AU - Pautrat-Lertora, A.
AU - Perez-Lozano, R.
AU - Ugarte, W.
PY - 2022
SP - 249
EP - 256
DO - 10.5220/0011548100003335
PB - SciTePress

- Science and Technology Publications, Lda.
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