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EMNLP 2025emnlpfindings

Linguistically-Controlled Paraphrase Generation

Mohamed Elgaar, Hadi Amiri

University of Massachusetts, Lowell · University of Massachusetts Lowell

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2025.findings-emnlp.1137 ↗

摘要

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.