TACTICS: Taxonomy-Aware Intelligent Corpus Sampling for Machine Translation
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 fro...
| Authors | Prasanth Bathala, Anubhav Shrimal, Sukhdeep Singh Kharbanda et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 6 October 2026 |
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