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Adapting the CycleGAN architecture for text style transfer

Lorandi, Michela orcid logoORCID: 0000-0002-6131-8763, A.Mohamed,, Maram and McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 (2023) Adapting the CycleGAN architecture for text style transfer. In: Irish Machine Vision and Image Processing Conference 2023, 30 Aug - 01 Sep 2023, Galway, Ireland.

Abstract
Text Style Transfer, the process of transforming text from one style to another, has gained significant attention in recent years due to its potential applications in various Natural Language Processing (NLP) tasks. In this paper, we present a novel approach for Text Style Transfer using a Cycle Generative Adversarial Network (CycleGAN). Our method utilizes the adversarial training framework of CycleGAN to learn the mapping between different text styles in an unsupervised manner, without the need for paired data. By leveraging the cycle consistency loss, our model is able to simultaneously learn style transfer mappings in both directions, allowing for bidirectional style transfer. We conduct experiments on the Yelp dataset to evaluate the effectiveness of our approach. Our results illustrate that our proposed TextCycleGAN achieves reasonable performance in terms of style transfer accuracy and fluency considering the simple architecture adopted in both generators and discriminators, while also providing bidirectional transfer capabilities (negative-positive and positive-negative).
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:CycleGAN; Text Style Transfer, Text Generation
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Irish Machine Vision and Image Processing Conference 2023, Proceedings. . Zenodo.
Publisher:Zenodo
Official URL:https://doi.org/10.5281/zenodo.8268838.
Copyright Information:© 2023 The Authors
Funders:Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No.18/CRT/6224, Insight SFI Centre for Data Analytics, Dublin City University under Grant No. SFI/12/RC/2289 P2
ID Code:28946
Deposited On:11 Sep 2023 09:00 by Michela Lorandi . Last Modified 26 Jan 2024 15:20
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