Version 1
: Received: 2 December 2023 / Approved: 4 December 2023 / Online: 4 December 2023 (06:53:06 CET)
How to cite:
Asanka, C.; Vatsalan, C.; Patel, R. Redefining Textual Dynamics for Enhanced Text Style Transfer. Preprints2023, 2023120144. https://doi.org/10.20944/preprints202312.0144.v1
Asanka, C.; Vatsalan, C.; Patel, R. Redefining Textual Dynamics for Enhanced Text Style Transfer. Preprints 2023, 2023120144. https://doi.org/10.20944/preprints202312.0144.v1
Asanka, C.; Vatsalan, C.; Patel, R. Redefining Textual Dynamics for Enhanced Text Style Transfer. Preprints2023, 2023120144. https://doi.org/10.20944/preprints202312.0144.v1
APA Style
Asanka, C., Vatsalan, C., & Patel, R. (2023). Redefining Textual Dynamics for Enhanced Text Style Transfer. Preprints. https://doi.org/10.20944/preprints202312.0144.v1
Chicago/Turabian Style
Asanka, C., Conti Vatsalan and Rodolfo Patel. 2023 "Redefining Textual Dynamics for Enhanced Text Style Transfer" Preprints. https://doi.org/10.20944/preprints202312.0144.v1
Abstract
Conventional text style transfer (TST) methodologies primarily utilize style classifiers to segregate the content and stylistic elements of text for effective style transformation. Despite the pivotal role of these classifiers, their influence on TST techniques remains largely unexplored. This study embarks on a detailed exploration of the limitations inherent in style classifiers within current TST frameworks. We reveal that these classifiers often inadequately comprehend sentence syntax, leading to diminished performance in TST models. In response, we introduce the Syntax-Enhanced Style Transfer (SEST) model, a groundbreaking approach incorporating a syntax-sensitive style classifier. This classifier ensures that the extracted style representations robustly encapsulate syntax nuances, enhancing TST effectiveness. Rigorous evaluations across diverse TST benchmarks demonstrate that SEST significantly surpasses contemporary models in performance. Additionally, our case studies highlight SEST's proficiency in producing syntactically coherent sentences that aptly retain original content.
Keywords
Syntax-Aware Text Transformation;Style Classifier Analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
