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    <abstract>Large-scale machine-translation (MT) systems are typically evaluated on random samples from a corpus whose distributional composition is an artifact of how it was assembled. Such a sample inherits the phenomena the collection happens to contain rather than the full space a system must handle, spanning rule-governed conventions (terminology, punctuation, currency formatting) and context-dependent phenomena (tone, honorifics, document-level coherence), and thus provides no coverage guarantee for assessing robustness. We propose TACTICS (Taxonomy-Aware Coverage-opTimized Intelligent Corpus Sampling), which recasts coverage as an explicit objective. TACTICS induces a hierarchical taxonomy from a locale style guide, classifies segments against it, and selects a fixed-budget subset jointly optimizing coverage of rare categories, document-level coherence, and distributional fidelity to the full corpus. Applied to MT evaluation across four translation directions, TACTICS improves coverage of rare categories over lexical and embedding-based selection. By targeting the phenomena that separate systems, TACTICS makes a fixed evaluation budget go further, recovering the true system ranking from far fewer segments than random sampling wherever a real quality gap exists and never signaling a difference where none exists.</abstract>
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%0 Conference Proceedings
%T TACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation
%A Bathala, Prasanth
%A Shrimal, Anubhav
%A Singh Kharbhanda, Sukhdeep
%A Lanka, Pradyumna
%A Dhaipule, Rohit
%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 bathala-etal-2026-tactics
%X Large-scale machine-translation (MT) systems are typically evaluated on random samples from a corpus whose distributional composition is an artifact of how it was assembled. Such a sample inherits the phenomena the collection happens to contain rather than the full space a system must handle, spanning rule-governed conventions (terminology, punctuation, currency formatting) and context-dependent phenomena (tone, honorifics, document-level coherence), and thus provides no coverage guarantee for assessing robustness. We propose TACTICS (Taxonomy-Aware Coverage-opTimized Intelligent Corpus Sampling), which recasts coverage as an explicit objective. TACTICS induces a hierarchical taxonomy from a locale style guide, classifies segments against it, and selects a fixed-budget subset jointly optimizing coverage of rare categories, document-level coherence, and distributional fidelity to the full corpus. Applied to MT evaluation across four translation directions, TACTICS improves coverage of rare categories over lexical and embedding-based selection. By targeting the phenomena that separate systems, TACTICS makes a fixed evaluation budget go further, recovering the true system ranking from far fewer segments than random sampling wherever a real quality gap exists and never signaling a difference where none exists.
%U https://aclanthology.org/2026.wmt-main.2/
%P 14-32

Markdown (Informal)

[TACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation](https://aclanthology.org/2026.wmt-main.2/) (Bathala et al., WMT 2026)

• TACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation (Bathala et al., WMT 2026)

ACL

• Prasanth Bathala, Anubhav Shrimal, Sukhdeep Singh Kharbhanda, Pradyumna Lanka, and Rohit Dhaipule. 2026. TACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation. In Proceedings of the Eleventh Conference on Machine Translation, pages 14–32, Budapest, Hungary. Association for Computational Linguistics.