LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control

Mohamed Elgaar, Hadi Amiri


Abstract
We introduce LINGCONV, an interactive toolkit for paraphrase generation enabling finegrained control over 40 specific lexical, syntactic, and discourse linguistic attributes. Users can directly manipulate target attributes using sliders, and with automatic imputation for unspecified attributes, simplifying the control process. Our adaptive Quality Control mechanism employs iterative refinement guided by line search to precisely steer the generation towards target attributes while preserving semantic meaning, overcoming limitations associated with fixed control strengths. Applications of LINGCONV include enhancing text accessibility by adjusting complexity for different literacy levels, enabling personalized communication through style adaptation, providing a valuable tool for linguistics and NLP research, and facilitating second language learning by tailoring text complexity. The system is available at https://mohdelgaar-lingconv.hf.space, with a demo video at https://youtu.be/wRBJEJ6EALQ.
Anthology ID:
2025.emnlp-demos.4
Volume:
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Ivan Habernal, Peter Schulam, Jörg Tiedemann
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
Note:
Pages:
42–51
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URL:
https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.4/
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Cite (ACL):
Mohamed Elgaar and Hadi Amiri. 2025. LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 42–51, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control (Elgaar & Amiri, EMNLP 2025)
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https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.4.pdf