@inproceedings{tan-etal-2025-benchmark,
    title = "A Benchmark for Translations Across Styles and Language Variants",
    author = "Tan, Xin  and
      Zou, Bowei  and
      Aw, AiTi",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.129/",
    doi = "10.18653/v1/2025.findings-emnlp.129",
    pages = "2389--2402",
    ISBN = "979-8-89176-335-7",
    abstract = "As machine translation (MT) rapidly advances in bridging global communication gaps, there is growing interest in variety-targeted translation for fine-grained language variants and specific translation styles. This translation variant aims to generate target outputs that are not only contextually accurate but also culturally sensitive. However, the lack of comprehensive evaluation benchmarks has hindered progress in this field. To bridge this gap, this work focuses on the translation across styles and language variants, aiming to establish a robust foundation for the automatic evaluation of fine-grained cultural and stylistic nuances, thereby fostering innovation in culturally sensitive translations. Specifically, we evaluate translations across four key dimensions: semantic preservation, cultural and regional specificity, expression style, and fluency at both the word and sentence levels. Through detailed human evaluations, we validate the high reliability of the proposed evaluation framework. On this basis, we thoroughly assess translations of state-of-the-art large language models (LLMs) for this task, highlighting their strengths and identifying areas for future improvement."
}