@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Linguistically-Controlled Paraphrase Generation
%A Elgaar, Mohamed
%A Amiri, Hadi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F elgaar-amiri-2025-linguistically
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.1137/
%P 20842-20864
Markdown (Informal)
[Linguistically-Controlled Paraphrase Generation](https://aclanthology.org/2025.findings-emnlp.1137/) (Elgaar & Amiri, Findings 2025)
ACL
- Mohamed Elgaar and Hadi Amiri. 2025. Linguistically-Controlled Paraphrase Generation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 20842–20864, Suzhou, China. Association for Computational Linguistics.