@inproceedings{ye-etal-2026-swan,
title = "{SWAN}: Semantic Watermarking with {A}bstract {M}eaning {R}epresentation",
author = "Ye, Ziping and
Dey, Gourab and
Christodoulopoulos, Christos and
Peris, Charith and
Ramakrishna, Anil and
Ruan, Weitong and
Galstyan, Aram and
Chang, Kai-Wei and
Gupta, Rahul and
Mehrabi, Ninareh",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1681/",
pages = "36304--36315",
ISBN = "979-8-89176-390-6",
abstract = "We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token selection preferences during text generation, SWAN embeds the signature directly in the sentence{'}s semantic representation. As the signature is encoded at the semantic structure level, any paraphrase that preserves meaning, automatically preserves the signature. SWAN is training-free: watermark injection is achieved by prompting an LLM to generate sentences guided by a selected AMR template while maintaining contextual coherence, and detection uses an off-the-shelf AMR parser followed by a simple one-proportion z-test. Empirical evaluation on the RealNews benchmark shows SWAN matches state-of-the-art detection performance on unaltered watermarked text, while significantly improving robustness against paraphrasing, increasing detection AUC by up to 13.9 percentage points compared to prior methods. These results demonstrate that SWAN{'}s approach of anchoring watermarks in AMR semantic structures provides a simple, effective, and prompt-based method for robust text provenance verification under paraphrasing, opening new avenues for semantic-level watermarking research."
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<abstract>We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token selection preferences during text generation, SWAN embeds the signature directly in the sentence’s semantic representation. As the signature is encoded at the semantic structure level, any paraphrase that preserves meaning, automatically preserves the signature. SWAN is training-free: watermark injection is achieved by prompting an LLM to generate sentences guided by a selected AMR template while maintaining contextual coherence, and detection uses an off-the-shelf AMR parser followed by a simple one-proportion z-test. Empirical evaluation on the RealNews benchmark shows SWAN matches state-of-the-art detection performance on unaltered watermarked text, while significantly improving robustness against paraphrasing, increasing detection AUC by up to 13.9 percentage points compared to prior methods. These results demonstrate that SWAN’s approach of anchoring watermarks in AMR semantic structures provides a simple, effective, and prompt-based method for robust text provenance verification under paraphrasing, opening new avenues for semantic-level watermarking research.</abstract>
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%0 Conference Proceedings
%T SWAN: Semantic Watermarking with Abstract Meaning Representation
%A Ye, Ziping
%A Dey, Gourab
%A Christodoulopoulos, Christos
%A Peris, Charith
%A Ramakrishna, Anil
%A Ruan, Weitong
%A Galstyan, Aram
%A Chang, Kai-Wei
%A Gupta, Rahul
%A Mehrabi, Ninareh
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ye-etal-2026-swan
%X We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token selection preferences during text generation, SWAN embeds the signature directly in the sentence’s semantic representation. As the signature is encoded at the semantic structure level, any paraphrase that preserves meaning, automatically preserves the signature. SWAN is training-free: watermark injection is achieved by prompting an LLM to generate sentences guided by a selected AMR template while maintaining contextual coherence, and detection uses an off-the-shelf AMR parser followed by a simple one-proportion z-test. Empirical evaluation on the RealNews benchmark shows SWAN matches state-of-the-art detection performance on unaltered watermarked text, while significantly improving robustness against paraphrasing, increasing detection AUC by up to 13.9 percentage points compared to prior methods. These results demonstrate that SWAN’s approach of anchoring watermarks in AMR semantic structures provides a simple, effective, and prompt-based method for robust text provenance verification under paraphrasing, opening new avenues for semantic-level watermarking research.
%U https://aclanthology.org/2026.acl-long.1681/
%P 36304-36315
Markdown (Informal)
[SWAN: Semantic Watermarking with Abstract Meaning Representation](https://aclanthology.org/2026.acl-long.1681/) (Ye et al., ACL 2026)
ACL
- Ziping Ye, Gourab Dey, Christos Christodoulopoulos, Charith Peris, Anil Ramakrishna, Weitong Ruan, Aram Galstyan, Kai-Wei Chang, Rahul Gupta, and Ninareh Mehrabi. 2026. SWAN: Semantic Watermarking with Abstract Meaning Representation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 36304–36315, San Diego, California, United States. Association for Computational Linguistics.