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Ieva Staliunaite

Ieva Staliunaite has 2 papers in our library, in 2024–2026, most of them on Machine translation, Evaluation & benchmarks, Bias, fairness & safety; often with Andreas Vlachos, Gisela Vallejo, Iryna Gurevych.

2 papers in our library · 2024–2026

OpenAlex · h-index 5 · 18 works · 88 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 31 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8008

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of CambridgeCambridge · United Kingdom2023–2026 · 4 paperscurrent🏢Huawei TechnologiesShenzhen · China2020–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksSocial Sciences · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workSociology and Political Science · 1 workSafety Research · 1 work

🧭 Topics 3Research subjects and languages the work is about.Bias, fairness & safety · 1 workEvaluation & benchmarks · 1 workMachine translation · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AmbiFC: Fact-Checking Ambiguous Claims with Evidence (2024 · 42 citations)
    Most fact-checking benchmarks expect one veracity verdict per claim. Real evidence, however, often allows several reasonable readings, and human annotators then disagree legitimately. AmbiFC addresses this gap. It is an English dataset of about 10k claims, built by having Mechanical Turk workers turn real yes/no…
  2. Uncertainty Quantification for Evaluating Gender Bias in Machine Translation (2026 · 1 citation)
    Standard gender-bias evaluation in machine translation relies on gender accuracy, which only works when the source sentence settles the referent's gender.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. AmbiFC: Fact-Checking Ambiguous Claims with EvidenceTransactions of the Association for Computational Linguistics (TACL)Max Glockner, Ieva Staliūnaitė, James Thorne, et al.2024 · 42 citations
  2. Comparative Study of Explainability Methods for Legal Outcome Prediction ↗Proceedings of the Natural Legal Language Processing Workshop 2024 · no summary yetIeva Staliunaite, Josef Valvoda, Ken Satoh2024 · 3 citations
  3. Uncertainty Quantification for Evaluating Gender Bias in Machine TranslationFindings of the Association for Computational LinguisticsIeva Staliunaite, Julius Cheng, Andreas Vlachos2026 · 1 citation
  4. Uncertainty Quantification for Evaluating Machine Translation Bias ↗arXiv:2507.18338 · no summary yetIeva Raminta Staliunaite, Julius Cheng, Andreas Vlachos2025 · 1 citation

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 31 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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