{"ID":2874415,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03809","arxiv_id":"2509.03809","title":"Align-then-Slide: A complete evaluation framework for Ultra-Long Document-Level Machine Translation","abstract":"Large language models (LLMs) have ushered in a new era for document-level machine translation (\\textit{doc}-mt), yet their whole-document outputs challenge existing evaluation methods that assume sentence-by-sentence alignment. We introduce \\textit{\\textbf{Align-then-Slide}}, a complete evaluation framework for ultra-long doc-mt. In the Align stage, we automatically infer sentence-level source-target correspondences and rebuild the target to match the source sentence number, resolving omissions and many-to-one/one-to-many mappings. In the n-Chunk Sliding Evaluate stage, we calculate averaged metric scores under 1-, 2-, 3- and 4-chunk for multi-granularity assessment. Experiments on the WMT benchmark show a Pearson correlation of 0.929 between our method with expert MQM rankings. On a newly curated real-world test set, our method again aligns closely with human judgments. Furthermore, preference data produced by Align-then-Slide enables effective CPO training and its direct use as a reward model for GRPO, both yielding translations preferred over a vanilla SFT baseline. The results validate our framework as an accurate, robust, and actionable evaluation tool for doc-mt systems.","short_abstract":"Large language models (LLMs) have ushered in a new era for document-level machine translation (\\textit{doc}-mt), yet their whole-document outputs challenge existing evaluation methods that assume sentence-by-sentence alignment. We introduce \\textit{\\textbf{Align-then-Slide}}, a complete evaluation framework for ultra-l...","url_abs":"https://arxiv.org/abs/2509.03809","url_pdf":"https://arxiv.org/pdf/2509.03809v1","authors":"[\"Jiaxin Guo\",\"Daimeng Wei\",\"Yuanchang Luo\",\"Xiaoyu Chen\",\"Zhanglin Wu\",\"Huan Yang\",\"Hengchao Shang\",\"Zongyao Li\",\"Zhiqiang Rao\",\"Jinlong Yang\",\"Hao Yang\"]","published":"2025-09-04T01:50:20Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}