Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

With the rise of social media and online communication, the issue of cyberbullying has
gained significant prominence. While extensive research is being conducted to develop …

Scarcity of parallel data causes formality style transfer models to have scarce success in
preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to …

Formality transfer seeks to adjust text formality without altering its core meaning, which
carries substantial implications across diverse domains like machine translation, dialogue …

While the field of style transfer (ST) has been growing rapidly, it has been hampered by a
lack of standardized practices for automatic evaluation. In this paper, we evaluate leading …

Ensuring that online discussions are civil and productive is a major challenge for social
media platforms. Such platforms usually rely both on users and on automated detection tools …

Physicians and patients often come from different backgrounds and have varying levels of
education, which can result in communication difficulties in the healthcare process. To …

Text Style Transfer (TST) evaluation is, in practice, inconsistent. Therefore, we conduct a
meta-analysis on human and automated TST evaluation and experimentation that …

We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using
machine translated data as well as gold aligned English sentences yields state-of-the-art …

Style transfer aims to rewrite a source text in a different target style while preserving its
content. We propose a novel approach to this task that leverages generic resources, and …