@inproceedings{tan-etal-2026-remedy,
    title = "Remedy-{R}: Generative Reasoning for Machine Translation Evaluation without Error Annotations",
    author = "Tan, Shaomu  and
      Mitani, Ryosuke  and
      Choudhary, Ritvik  and
      Wu, Qiyu  and
      Sekiya, Toshiyuki  and
      Monz, Christof",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.364/",
    pages = "7374--7398",
    ISBN = "979-8-89176-395-1",
    abstract = "Over the years, scalar MT metrics have advanced rapidly on benchmarks. Yet they remain black boxes, offering little insight into their decisions and sometimes degrading under out-of-distribution inputs. We introduce Remedy-R, a reasoning-driven generative MT metric trained with reinforcement learning from pairwise translation preferences, without requiring error-span annotations or distillation from closed LLMs. Unlike scalar MT metrics that only outputs translation quality scores, Remedy-R produces step-by-step analyses of accuracy, fluency, and completeness, enabling more interpretable assessments. With only 60K pairwise training samples across two language pairs, Remedy-R remains competitive with top scalar metrics and GPT-4-based judges on WMT22{--}24 metric benchmarks, generalizes to other languages, and shows strong robustness on OOD stress tests. Moreover, Remedy-R generates self-reflective feedback that can be reused for translation refinement. We validate the faithfulness of such feedback with GPT-4 and show that a simple evaluate{--}revise pipeline leveraging Remedy-R{'}s analyses consistently improves translation quality across diverse models without any task-specific tuning."
}