@inproceedings{anugraha-etal-2024-metametrics,
    title = "{M}eta{M}etrics-{MT}: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration",
    author = "Anugraha, David  and
      Kuwanto, Garry  and
      Susanto, Lucky  and
      Wijaya, Derry Tanti  and
      Winata, Genta",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.wmt-1.32/",
    doi = "10.18653/v1/2024.wmt-1.32",
    pages = "459--469",
    abstract = "We present MetaMetrics-MT, an innovative metric designed to evaluate machine translation (MT) tasks by aligning closely with human preferences through Bayesian optimization with Gaussian Processes. MetaMetrics-MT enhances existing MT metrics by optimizing their correlation with human judgments. Our experiments on the WMT24 metric shared task dataset demonstrate that MetaMetrics-MT outperforms all existing baselines, setting a new benchmark for state-of-the-art performance in the reference-based setting. Furthermore, it achieves comparable results to leading metrics in the reference-free setting, offering greater efficiency."
}