@inproceedings{himmi-etal-2024-enhanced,
    title = "Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation",
    author = "Himmi, Anas  and
      Staerman, Guillaume  and
      Picot, Marine  and
      Colombo, Pierre  and
      Guerreiro, Nuno M",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-main.1033/",
    doi = "10.18653/v1/2024.emnlp-main.1033",
    pages = "18573--18583",
    abstract = "Hallucinated translations pose significant threats and safety concerns when it comes to practical deployment of machine translation systems. Previous research works have identified that detectors exhibit complementary performance {---} different detectors excel at detecting different types of hallucinations. In this paper, we propose to address the limitations of individual detectors by combining them and introducing a straightforward method for aggregating multiple detectors. Our results demonstrate the efficacy of our aggregated detector, providing a promising step towards evermore reliable machine translation systems."
}