yztian/MENT

Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation (ACL 2026 Main, Oral)

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We introduce MENT (Meta-Evaluation dataset of Non-Literal Translation), a human-annotated meta-evaluation dataset to systematically assess MT evaluation metrics.

Citation

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@inproceedings{tian-etal-2026-beyond,
    title = "Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation",
    author = "Tian, Yanzhi  and
      Wang, Cunxiang  and
      Liu, Zeming  and
      Huang, Heyan  and
      Yu, Wenbo  and
      Song, Dawei  and
      Tang, Jie  and
      Guo, Yuhang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.205/",
    doi = "10.18653/v1/2026.acl-long.205",
    pages = "4490--4524",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Network Services, literature etc. In these scenarios, translations often require handling non-literal expressions, leading to the inaccuracy of MT metrics. To systematically investigate the reliability of MT metrics, we first curate a meta-evaluation dataset focused on non-literal translations, namely MENT. MENT encompasses four non-literal translation domains and features source sentences paired with translations from diverse MT systems, with 7,530 human-annotated scores on translation quality. Experimental results reveal the inaccuracies of traditional MT metrics and the limitations of LLM-as-a-Judge, particularly the knowledge cutoff and score inconsistency problem. To mitigate these limitations, we propose RATE, a novel agentic translation evaluation framework, centered by a reflective Core Agent that dynamically invokes specialized sub-agents. Experimental results indicate the efficacy of RATE, achieving an improvement of at least 3.2 points in combined system- and segment-level correlation with human judgments compared with current methods. Further experiments demonstrate the robustness of RATE to general-domain MT evaluation. Code and dataset are available at: \url{https://github.com/BITHLP/RATE}."
}