Recent advances in neural machine translation (MT) have spurred increased interest in evaluating translations beyond the sentence level, making it possible to assess discourse-level phenomena related to coherence and consistency. While existing metrics can be applied to multi-sentence spans, it remains unclear whether their scores truly capture document-level quality. We introduce MetaDocEval, an automatic contrastive test set for evaluating MT metrics across three language pairs (en–fr, en–es, en–de) when applied at the document-level. It targets a range of discourse-level phenomena and potential problems linked to translation at the document level. To evaluate how metrics behave as a function of context size, we apply them under a sliding-window protocol, varying the input from single sentences up to full documents. Our experiments show that no current metric genuinely captures document-level coherence: reference-based metrics overfit lexical overlap, reference+source metrics gain little from added context, reference-free encoders show brief context sensitivity before degrading on longer spans, and LLM-based scorers collapse beyond short inputs. A key finding is that reference access can be actively harmful for detecting discourse-level errors. Using short windows (≈ 3 sentences) offers the best trade-off between discourse error detection and score dilution.

Terminology evaluation in machine translation (MT) usually assumes a single correct target form per source term, yet human translators routinely introduce variation that current metrics penalize as inconsistency. We examine how to account for this variation in document-level MT evaluation in English–French scientific translation, combining glossary-based accuracy, translation consistency, and a new cross-term variation (CTV) diagnostic measure that captures whether variation relationships are preserved across languages. On two parallel corpora translated by four MT systems, we find that (1) MT systems generate less target-side variation than human translators; (2) transfer patterns strongly depend on the variation type; (3) consistency rankings vary with the choice of metric; and (4) constraining MT with a glossary improves accuracy and consistency but degrades CTV by suppressing valid variation. We argue for variation-aware evaluation that conditions consistency penalties on whether target-side variation mirrors source-side variation.

This paper is a short presentation of MaTOS, a project focusing on the automatic translation of scholarly documents. Its main aims are threefold: (a) to develop resources (term lists and corpora) for high-quality machine translation; (b) to study methods for translating complete, structured documents in a cohesive and consistent manner; (c) to propose novel metrics to evaluate machine translation in technical domains. Publications and resources are available on the project web site: https://anr-matos.gihub.io.