mlsanityData to 14 Oct 2026

Pearmut: Human Evaluation of Translation Made Trivial

arXiv 2601.02933 ↗First posted 6 Jan 2026cs.CL cs.HCcited by 11

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Abstract (arXiv)

Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics because it is notoriously complex and slow to set up with existing tools with substantial engineering and operational overhead. We introduce Pearmut, a lightweight yet feature-rich platform that makes end-to-end human evaluation as easy to run as automatic evaluation. Pearmut removes common entry barriers and provides support for evaluating multilingual tasks, with a particular focus on machine translation. The platform implements standard evaluation protocols, including DA, ESA, and MQM, and is extensible to support new protocols. It features document-level context, absolute and contrastive evaluation, attention checks, ESAAI pre-annotations and both static and dynamic assignment strategies. Pearmut enables reliable human evaluation to become a practical, routine component of model development and diagnosis rather than an occasional effort.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 6 Jan 2026.

Builds on

16 of 36 references.

References · 16

  1. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  2. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
  3. 2021Dynabench: Rethinking Benchmarking in NLPcited by 459
  4. 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 338
  5. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 178
  6. 2021Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Paperscited by 71
  7. 2018RankME: Reliable Human Ratings for Natural Language Generationcited by 69
  8. 2022POTATO: The Portable Text Annotation Toolcited by 64
  9. 2024Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuraciescited by 40
  10. 2018Efficient Online Scalar Annotation with Bounded Supportcited by 27
  11. 2021GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generationcited by 20
  12. 2024AI-Assisted Human Evaluation of Machine Translationcited by 20
4 more
  1. 2022Toward More Effective Human Evaluation for Machine Translationcited by 10
  2. 2025Enhancing Human Evaluation in Machine Translation with Comparative Judgmentcited by 7
  3. 2025Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMscited by 7
  4. 2024factgenie: A Framework for Span-based Evaluation of Generated Textscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 11.

11 papers.