Discourse Dependency: A Continuous Criterion for Translation Difficulty
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Computer Science > Computation and Language
arXiv:2609.04959 (cs)
[Submitted on 4 Sep 2026]
Title:Discourse Dependency: A Continuous Criterion for Translation Difficulty
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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 >= 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.
| Comments: | Accepted to EMNLP 2026 (Main Conference) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.04959 [cs.CL] |
| (or arXiv:2609.04959v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.04959
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Discourse Dependency: A Continuous Criterion for Translation Difficulty, by Ahrii Kim and 2 other authors
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