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.
@article{arxiv.2411.00390,
title = {MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration},
author = {David Anugraha and Garry Kuwanto and Lucky Susanto and Derry Tanti Wijaya and Genta Indra Winata},
journal= {arXiv preprint arXiv:2411.00390},
year = {2024}
}