An Analysis of Source-Side Diversity in Machine Translation Evaluation

Esther Ploeger; Johannes Bjerva; Dong Nguyen; Robert Östling

An Analysis of Source-Side Diversity in Machine Translation Evaluation

Esther Ploeger, Johannes Bjerva, Dong Nguyen, Robert Östling

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Abstract

Benchmarks are central to assessing progress in machine translation (MT), yet the content of widely used MT test sets is increasingly scrutinized. Concerns about data contamination and benchmark difficulty have gained traction, but the role of source-side diversity remains largely overlooked. Many popular benchmarks contain overlapping source items and even exact duplicates, raising the question of how such repetition affects evaluation quality. We empirically examine how source-side diversity shapes MT evaluation. First we measure the inter-source diversity of nine popular MT datasets. Next, our downstream analysis, based on WMT24, suggests that benchmarks with low diversity may be less discriminative and may provide artificially narrow confidence intervals. Ultimately, we call for greater attention to inter-source diversity in general MT benchmark design.

Anthology ID:: 2026.wmt-main.31
Volume:: Proceedings of the Eleventh Conference on Machine Translation
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venues:: WMT | WS
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 554–573
Language:
URL:: https://aclanthology.org/2026.wmt-main.31/
DOI:
Bibkey:
Cite (ACL):: Esther Ploeger, Johannes Bjerva, Dong Nguyen, and Robert Östling. 2026. An Analysis of Source-Side Diversity in Machine Translation Evaluation. In Proceedings of the Eleventh Conference on Machine Translation, pages 554–573, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: An Analysis of Source-Side Diversity in Machine Translation Evaluation (Ploeger et al., WMT 2026)
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PDF:: https://aclanthology.org/2026.wmt-main.31.pdf

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@inproceedings{ploeger-etal-2026-analysis,
    title = "An Analysis of Source-Side Diversity in Machine Translation Evaluation",
    author = {Ploeger, Esther  and
      Bjerva, Johannes  and
      Nguyen, Dong  and
      {\"O}stling, Robert},
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.wmt-main.31/",
    pages = "554--573",
    ISBN = "979-8-89176-474-3",
    abstract = "Benchmarks are central to assessing progress in machine translation (MT), yet the content of widely used MT test sets is increasingly scrutinized. Concerns about data contamination and benchmark difficulty have gained traction, but the role of source-side diversity remains largely overlooked. Many popular benchmarks contain overlapping source items and even exact duplicates, raising the question of how such repetition affects evaluation quality. We empirically examine how source-side diversity shapes MT evaluation. First we measure the inter-source diversity of nine popular MT datasets. Next, our downstream analysis, based on WMT24, suggests that benchmarks with low diversity may be less discriminative and may provide artificially narrow confidence intervals. Ultimately, we call for greater attention to inter-source diversity in general MT benchmark design."
}

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%0 Conference Proceedings
%T An Analysis of Source-Side Diversity in Machine Translation Evaluation
%A Ploeger, Esther
%A Bjerva, Johannes
%A Nguyen, Dong
%A Östling, Robert
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F ploeger-etal-2026-analysis
%X Benchmarks are central to assessing progress in machine translation (MT), yet the content of widely used MT test sets is increasingly scrutinized. Concerns about data contamination and benchmark difficulty have gained traction, but the role of source-side diversity remains largely overlooked. Many popular benchmarks contain overlapping source items and even exact duplicates, raising the question of how such repetition affects evaluation quality. We empirically examine how source-side diversity shapes MT evaluation. First we measure the inter-source diversity of nine popular MT datasets. Next, our downstream analysis, based on WMT24, suggests that benchmarks with low diversity may be less discriminative and may provide artificially narrow confidence intervals. Ultimately, we call for greater attention to inter-source diversity in general MT benchmark design.
%U https://aclanthology.org/2026.wmt-main.31/
%P 554-573

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Markdown (Informal)

[An Analysis of Source-Side Diversity in Machine Translation Evaluation](https://aclanthology.org/2026.wmt-main.31/) (Ploeger et al., WMT 2026)

An Analysis of Source-Side Diversity in Machine Translation Evaluation (Ploeger et al., WMT 2026)

ACL

Esther Ploeger, Johannes Bjerva, Dong Nguyen, and Robert Östling. 2026. An Analysis of Source-Side Diversity in Machine Translation Evaluation. In Proceedings of the Eleventh Conference on Machine Translation, pages 554–573, Budapest, Hungary. Association for Computational Linguistics.