@InProceedings{pong:2026:wmt,
  author    = {Pong, Benjamin},
  title     = {Post-hoc Correction of Machine Translation Error Span Predictions},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {1730--1736},
  abstract  = {This paper presents an unsupervised quality estimation metric for machine translation that combines both neural and LLM models to identify and classify error spans for machine translations. As a submission to the WMT2026 Automated Evaluation Shared Task 1, this system builds on XCOMET-XL by applying offsets to the logits produced by the neural metric, post-hoc, to address the long-tailed probability problem, and uses an LLM as a second-stage to refine these predictions. Results show that XCOMET-XL with logits offset alone shows a boost in recall and character-level F1 scores for error span classifications across multiple language pairs, surpassing state-of-the-art baseline (i.e XCOMET-XL) and LLM-based approaches. This system also comprises a second-stage where a reasoning LLM-judge is employed to audit the error spans predicted by the logits-adjusted XCOMET metric, whose goal is to reduce spurious error classifications and improve overall precision. Results show that using LLM-judge to refine error spans provides no measurable improvements over predictions of the logits-adjusted XCOMET metric.},
  url       = {https://aclanthology.org/2026.wmt-1.106}
}