@inproceedings{ca88935534664887931bbd91c15afb7f,
title = "Unsupervised Style Transfer of Modern Hebrew using Generative Language Modeling and Zero-Shot Prompting",
abstract = "Style transfer is one of the most intriguing hallmarks of natural language processing. It involves the semantic preserving conversion of artistic 'style'. Style transfer of the Hebrew language is an exceptionally challenging task due to the language's intricate morphology, inflectional structure, and orthography, which have undergone significant transformations throughout its history. In this work, we present the first generative language model for unsupervised textual style transfer for modern Hebrew, which rewrites sentences in a target style in the absence of parallel style corpora. We create a pseudo-parallel corpus through back translation, fine-tunes a pre-trained Hebrew language model, and leverages zero-shot learning. Our results demonstrate the first significant results in Hebrew style transfer in terms of transfer accuracy, semantic similarity, and fluency.",
keywords = "Computational Literary Studies, Hebrew Language, Language Model, Machine Learning, Modern Hebrew Literature, Natural Language Processing, Style Transfer",
author = "Pavel Kaganovich and Ophir Munz-Manor and Tsur, \{Elishai Ezra\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",
year = "2023",
doi = "10.1109/BigData59044.2023.10386846",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4646--4653",
editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, \{Jerry Chun-Wei\} and Rakesh Agrawal",
booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",
address = "United States",
}