StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples
Ajay Patel, Jiacheng Zhu, Justin Qiu, Zachary Horvitz, Marianna Apidianaki, Kathleen McKeown, Chris Callison-Burch
Abstract
Style representations aim to embed texts with similar writing styles closely and texts with different styles far apart, regardless of content. However, the contrastive triplets often used for training these representations may vary in both style and content, leading to potential content leakage in the representations. We introduce StyleDistance, a novel approach to training stronger content-independent style embeddings. We use a large language model to create a synthetic dataset of near-exact paraphrases with controlled style variations, and produce positive and negative examples across 40 distinct style features for precise contrastive learning. We assess the quality of our synthetic data and embeddings through human and automatic evaluations. StyleDistance enhances the content-independence of style embeddings, which generalize to real-world benchmarks and outperform leading style representations in downstream applications.
BibTeX
@inproceedings{patel-etal-2025-styledistance,
title = "{S}tyle{D}istance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples",
author = "Patel, Ajay and
Zhu, Jiacheng and
Qiu, Justin and
Horvitz, Zachary and
Apidianaki, Marianna and
McKeown, Kathleen and
Callison-Burch, Chris",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.436/",
pages = "8662--8685",
ISBN = "979-8-89176-189-6"
}