{
 "id": "yztian/MENT",
 "owner": "yztian",
 "name": "MENT",
 "downloads": 30,
 "likes": 0,
 "last_modified": "2026-08-31T12:33:33.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
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 "gated": "auto",
 "tasks": [
  "translation"
 ],
 "citation": "@inproceedings{tian-etal-2026-beyond,\n    title = \"Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation\",\n    author = \"Tian, Yanzhi  and\n      Wang, Cunxiang  and\n      Liu, Zeming  and\n      Huang, Heyan  and\n      Yu, Wenbo  and\n      Song, Dawei  and\n      Tang, Jie  and\n      Guo, Yuhang\",\n    editor = \"Liakata, Maria  and\n      Moreira, Viviane P.  and\n      Zhang, Jiajun  and\n      Jurgens, David\",\n    booktitle = \"Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2026\",\n    address = \"San Diego, California, United States\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2026.acl-long.205/\",\n    doi = \"10.18653/v1/2026.acl-long.205\",\n    pages = \"4490--4524\",\n    ISBN = \"979-8-89176-390-6\",\n    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,",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "5e7f425a31b2929dcc960332674f9fd320d1199f",
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}