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    <abstract>Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeasured axis is referential reach, the distance a segment must look back into its document to resolve the entities and pronouns it contains. We formalize this as discourse dependency (DDP), a metric-free, source-side measure computed from named entity re-mentions and pronominal coreference. Validated against gold coreference, DDP errs one-sidedly in 99.2% of segments, so a high-DDP segment is certified to require long-range context. Applying DDP to WMT24++ and WMT25 shows that both are heavily skewed toward low-DDP segments, which domain labels do not distinguish. Building on DDP, we compare five context injection strategies in an English-Korean post-editing setup, varying context size and selection. As DDP grows, no strategy keeps pace with human post-editing. On segments with DDP \ensuremath\geq 15 raters prefer human translations, while automatic metrics register no difference. As frontier systems saturate aggregate scores, DDP shifts evaluation from how well models score to how far they can reach.</abstract>
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%0 Conference Proceedings
%T Discourse Dependency: A Continuous Criterion for Translation Difficulty
%A Kim, Ahrii
%A Park, Chanjun
%A Kim, Seong-heum
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-etal-2026-discourse
%X Recent calls for harder machine translation benchmarks have not clarified what difficulty should mean. We argue that one meaningful and currently unmeasured axis is referential reach, the distance a segment must look back into its document to resolve the entities and pronouns it contains. We formalize this as discourse dependency (DDP), a metric-free, source-side measure computed from named entity re-mentions and pronominal coreference. Validated against gold coreference, DDP errs one-sidedly in 99.2% of segments, so a high-DDP segment is certified to require long-range context. Applying DDP to WMT24++ and WMT25 shows that both are heavily skewed toward low-DDP segments, which domain labels do not distinguish. Building on DDP, we compare five context injection strategies in an English-Korean post-editing setup, varying context size and selection. As DDP grows, no strategy keeps pace with human post-editing. On segments with DDP \ensuremath\geq 15 raters prefer human translations, while automatic metrics register no difference. As frontier systems saturate aggregate scores, DDP shifts evaluation from how well models score to how far they can reach.
%U https://aclanthology.org/2026.emnlp-main.431/
%P 9887-9912

Markdown (Informal)

[Discourse Dependency: A Continuous Criterion for Translation Difficulty](https://aclanthology.org/2026.emnlp-main.431/) (Kim et al., EMNLP 2026)

• Discourse Dependency: A Continuous Criterion for Translation Difficulty (Kim et al., EMNLP 2026)

ACL

• Ahrii Kim, Chanjun Park, and Seong-heum Kim. 2026. Discourse Dependency: A Continuous Criterion for Translation Difficulty. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9887–9912, Budapest, Hungary. Association for Computational Linguistics.