@inproceedings{elgaar-amiri-2025-linguistically,
    title = "Linguistically-Controlled Paraphrase Generation",
    author = "Elgaar, Mohamed  and
      Amiri, Hadi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1137/",
    doi = "10.18653/v1/2025.findings-emnlp.1137",
    pages = "20842--20864",
    ISBN = "979-8-89176-335-7",
    abstract = "Controlled paraphrase generation produces paraphrases that preserve meaning while allowing precise control over linguistic attributes of the output. We introduce LingConv, an encoder-decoder framework that enables fine-grained control over 40 linguistic attributes in English. To improve reliability, we introduce a novel inference-time quality control mechanism that iteratively refines attribute embeddings to generate paraphrases that closely match target attributes without sacrificing semantic fidelity. LingConv reduces attribute error by up to 34{\%} over existing models, with the quality control mechanism contributing an additional 14{\%} improvement."
}