@InProceedings{lee:2026:wmt2,
  author    = {Lee, Soyoung},
  title     = {PragmaSpan at WMT26: Taxonomy-Guided Few-Shot Error Span Detection and an Empirical Analysis of MPP},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {1685--1695},
  abstract  = {We present PragmaSpan, a training-free, few-shot GPT-5.5 error-span detector with a seven-axis diagnostic taxonomy for WMT26 Task 1, wrapped in a reliability-oriented harness using incomplete-response-aware retries, deterministic parsing, offset recovery, exact-span deduplication, and independent artifact validation. The taxonomy provides a domain-sensitive diagnostic representation: its interpersonal and discourse axes track domain conversationality and match gold errors at least as often as its traditional axes, with no significant detection-F difference from a standard Multidimensional Quality Metrics (MQM) category tree. We use the system to characterize the severity-weighted Match with Partial overlap and Partial credit (MPP) metric. Across prompt and model comparisons, human-agreement calibration, and post-hoc refinement, we find (i) prompt variants do not differ significantly on our sample, while GPT-4.1-to-GPT-5.5 gains exceed differences among variants; (ii) human–human MPP is low on our multi-annotator surrogate sample, and system–human agreement is not significantly different from this empirical baseline; (iii) improvements in matched-pair severity or boundary accuracy can coincide with lower MPP, demonstrating the need to interpret MPP jointly with matched-pair diagnostics and match coverage; and (iv) a calibration gain on a single-annotator MQM benchmark does not replicate on a multi-annotator Error Span Annotation benchmark. Interpreting error-span improvements therefore requires complementary metrics and annotation-regime checks.},
  url       = {https://aclanthology.org/2026.wmt-1.101}
}