---
title: "SwissGov-RSD — paper by Wastl et al. (2026)"
description: "The paper tackles the recognition of semantic differences (RSD): finding which tokens in two related texts do not mean the… 46 references."
authors: ["Michelle Wastl", "Jannis Vamvas", "Rico Sennrich"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1437/"
doi: "10.18653/v1/2026.acl-long.1437"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wastl-2026-swissgov-rsd-human-annotated-cross/"
---
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# SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents

**Authors:** Michelle Wastl, [Jannis Vamvas](https://md.research.elamur.ai/free/en/authors/jannis-vamvas/) and [Rico Sennrich](https://md.research.elamur.ai/free/en/authors/rico-sennrich/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1437/) · [PDF](https://aclanthology.org/2026.acl-long.1437.pdf) · [DOI 10.18653/v1/2026.acl-long.1437](https://doi.org/10.18653/v1/2026.acl-long.1437) · [OpenAlex](https://openalex.org/W7166859256)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper tackles the recognition of semantic differences (RSD): finding which tokens in two related texts do not mean the same thing. It focuses on cross-lingual, document-length inputs, where earlier evaluation depended on the synthetically built iSTS-RSD benchmark. The authors crawled the multilingual Swiss government portal admin.ch. Its language versions often diverge because of translation mistakes or updates that were not synchronized across languages. From this they built SwissGov-RSD, a multi-parallel set of English–German, English–French and English–Italian documents. Student annotators marked difference spans on an inverted iSTS-style scale, where 0 means equivalent and 5 means entirely unrelated. The authors then tested LLMs and encoder models in unsupervised, few-shot and fine-tuned setups, scoring them with token-level Spearman correlation. Every system scored far lower on this naturalistic data than on synthetic benchmarks. LLMs also produced malformed or truncated outputs and missed differences on long inputs. The results suggest that synthetic RSD benchmarks overstate real-world ability, and that new methods are needed for practical tasks such as checking multilingual content consistency.

## Key points
- **Dataset size:** SwissGov-RSD contains 224 document pairs per language pair (EN–DE, EN–FR, EN–IT), drawn from 235 filtered multi-parallel documents. Average document length is about 386–437 whitespace tokens, and each language pair totals roughly 173k–196k tokens.
- **Annotation setup:** Two annotators worked on each language pair, with 25 documents annotated by both. Token-level Spearman agreement between annotators was 56.14 (EN–DE), 43.61 (EN–FR) and 65.09 (EN–IT), which shows that the task is hard even for humans.
- **Benchmark coverage:** The authors evaluated 6 LLMs and 12 encoder models using 3 approaches, including DiffAlign and fine-tuned regressors, on both SwissGov-RSD and iSTS-RSD. Spearman scores dropped by up to 78 points on the naturalistic data.
- **LLM results:** Fine-tuned GPT-4o-mini performed best on iSTS-RSD but lost the most on SwissGov-RSD. Proprietary models outperformed open-weight ones. LLMs suffered from sequence-length mismatches and tended to under-label differences near document ends. Llama-3.1 405B often failed to produce complete output on inputs longer than about 1200 tokens.
- **Release:** Code is on GitHub (ZurichNLP/SwissGov-RSD) and data is on Hugging Face. The labels are released under CC BY, while copyright of the texts stays with the Swiss federal authorities.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Zurich](https://md.research.elamur.ai/free/en/universities/university-of-zurich/) — University, Zurich, Switzerland · 80 works · 12,260 citations

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## Cite

Cite as: Michelle Wastl, Jannis Vamvas and Rico Sennrich. 2026. SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wastl2026swissgov,
  title = {{SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents}},
  author = {Michelle Wastl, Jannis Vamvas and Rico Sennrich},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1437},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1437}
}
```

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*Updated 2 November 2026*