@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://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.436/",
    pages = "8662--8685",
    ISBN = "979-8-89176-189-6",
    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."
}