Pearmut: Human Evaluation of Translation Made Trivial
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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
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- 2024AI-Assisted Human Evaluation of Machine Translationcited by 20
4 more
- 2022Toward More Effective Human Evaluation for Machine Translationcited by 10
- 2025Enhancing Human Evaluation in Machine Translation with Comparative Judgmentcited by 7
- 2025Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMscited by 7
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Built on by
Citing papers per year: 2026: 11.
11 papers.
- 2026PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translationcited by 1
- 2026Translating Under Pressure: Domain-Aware LLMs for Crisis Communicationcited by 1
- 2026Contrastive ESA: Human Evaluation of Multiple Translations at Oncecited by 1
- 2026Dynamically Allocating Evaluation Effort for Model Rankingcited by 1
- 2026Augmenting Text to Increase Translation Difficultycited by 1
- 2026Last Translation Benchmarkcited by 1
- 2026AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documentscited by 0
- 2026The Role of Disfluencies in Speech Translationcited by 0
- 2026The Blindness of Document-Level Translation Evaluationcited by 0
- 2026Generating Adversarial Texts for Machine Translation via GRPOcited by 0
- 2026Controlling Backchannels in Streamable Full-duplex Modelscited by 0
- 2026Controlling Backchannels in Streamable Full-duplex Models24 Sep 2026
- 2026Generating Adversarial Texts for Machine Translation via GRPO5 Sep 2026
- 2026The Blindness of Document-Level Translation Evaluation5 Sep 2026
- 2026Last Translation Benchmark3 Sep 2026
- 2026Augmenting Text to Increase Translation Difficulty16 Aug 2026
- 2026Dynamically Allocating Evaluation Effort for Model Ranking4 Aug 2026
- 2026The Role of Disfluencies in Speech Translation3 Aug 2026
- 2026Contrastive ESA: Human Evaluation of Multiple Translations at Once29 Jul 2026
- 2026AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documents14 May 2026
- 2026Translating Under Pressure: Domain-Aware LLMs for Crisis Communication29 Apr 2026
- 2026PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation25 Jan 2026