@inproceedings{ouzerrout-2025-uter,
    title = "{UTER}: Capturing the Human Touch in Evaluating Morphologically Rich and Low-Resource Languages",
    author = "Ouzerrout, Samy",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, U.S.A.",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.loresmt-1.3/",
    pages = "16--23",
    ISBN = "979-8-89176-230-5",
    abstract = "We introduce UTER, a novel automatic translation evaluation metric specifically designed for morphologically complex languages. Unlike traditional TER approaches, UTER incorporates a reordering algorithm and leverages the S{\o}rensen-Dicse similarity measure to better account for morphological variations.Tested on morphologically rich and low resource languages from the WMT22 dataset, such as Finnish, Estonian, Kazakh, and Xhosa, UTER delivers results that align more closely with human direct assessments (DA) and outperforms benchmark metrics, including chrF and METEOR. Furthermore, its effectiveness has also been demonstrated on languages with complex writing systems, such as Chinese and Japanese, showcasing its versatility and robustness."
}