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        <title>Is Human Annotation Necessary? Iterative MBR Distillation for Error Span Detection in Machine Translation</title>
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    <abstract>Error Span Detection (ESD) is a crucial subtask in Machine Translation (MT) evaluation, aiming to identify the location and severity of translation errors. While fine-tuning models on human-annotated data improves ESD performance, acquiring such data is expensive and prone to inconsistencies among annotators. To address this, we propose a novel self-evolution framework based on Minimum Bayes Risk (MBR) decoding, named Iterative MBR Distillation for ESD, which eliminates the reliance on human annotations by leveraging an off-the-shelf LLM to generate pseudo-labels. Extensive experiments on the WMT Metrics Shared Task datasets demonstrate that models trained solely on these self-generated pseudo-labels outperform both unadapted base model and supervised baselines trained on human annotations at the system and span levels, while maintaining competitive sentence-level performance.</abstract>
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%0 Conference Proceedings
%T Is Human Annotation Necessary? Iterative MBR Distillation for Error Span Detection in Machine Translation
%A Lyu, Boxuan
%A Song, Haiyue
%A Qu, Zhi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lyu-etal-2026-human
%X Error Span Detection (ESD) is a crucial subtask in Machine Translation (MT) evaluation, aiming to identify the location and severity of translation errors. While fine-tuning models on human-annotated data improves ESD performance, acquiring such data is expensive and prone to inconsistencies among annotators. To address this, we propose a novel self-evolution framework based on Minimum Bayes Risk (MBR) decoding, named Iterative MBR Distillation for ESD, which eliminates the reliance on human annotations by leveraging an off-the-shelf LLM to generate pseudo-labels. Extensive experiments on the WMT Metrics Shared Task datasets demonstrate that models trained solely on these self-generated pseudo-labels outperform both unadapted base model and supervised baselines trained on human annotations at the system and span levels, while maintaining competitive sentence-level performance.
%U https://aclanthology.org/2026.findings-emnlp.169/
%P 3567-3577

Markdown (Informal)

[Is Human Annotation Necessary? Iterative MBR Distillation for Error Span Detection in Machine Translation](https://aclanthology.org/2026.findings-emnlp.169/) (Lyu et al., Findings 2026)

• Is Human Annotation Necessary? Iterative MBR Distillation for Error Span Detection in Machine Translation (Lyu et al., Findings 2026)

ACL

• Boxuan Lyu, Haiyue Song, and Zhi Qu. 2026. Is Human Annotation Necessary? Iterative MBR Distillation for Error Span Detection in Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 3567–3577, Budapest, Hungary. Association for Computational Linguistics.