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title: "Rico Sennrich — papers, page 2 · University of Zurich"
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# Rico Sennrich · page 2

30 papers in our library · 2012–2026 · page 2 of 2

[The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/rico-sennrich/)

## Papers

The most cited first (OpenAlex and our library).

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26. **[Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets](https://md.research.elamur.ai/free/en/papers/moghe-2025-machine-translation-meta-evaluation-through/)** *(2025 · 28 citations)* Automatic MT metrics are usually validated by how well they correlate with human judgments, averaged over large test sets.
27. **[Parity-Aware Byte-Pair Encoding: Improving Cross-lingual Fairness in Tokenization](https://md.research.elamur.ai/free/en/papers/foroutan-2026-parity-aware-byte-pair-encoding/)** *(2026 · 12 citations)* Subword tokenizers trained with frequency-driven objectives, such as standard Byte Pair Encoding, favor languages that dominate the training data.
28. **[CommonMorph: Participatory Morphological Documentation Platform](https://md.research.elamur.ai/free/en/papers/mahmudi-2026-commonmorph-participatory-morphological-documentation-platform/)** *(2026)* Building inflectional morphology datasets usually requires trained linguists, a lot of manual work and substantial funding, so most low-resource and endangered languages lack them.
29. **[Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias](https://md.research.elamur.ai/free/en/papers/schuhmacher-2026-information-representation-fairness-long-document/)** *(2026)* Embedding-based search can only surface information that a document's vector actually encodes. This paper asks whether every part of a long, multi-segment document is represented equally. The authors build a permutation-based diagnostic on a comparable Wikipedia corpus in six languages. Topic-aligned articles are…
30. **[SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents](https://md.research.elamur.ai/free/en/papers/wastl-2026-swissgov-rsd-human-annotated-cross/)** *(2026)* The paper tackles the recognition of semantic differences (RSD): finding which tokens in two related texts do not mean the same thing.

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