Enhanced Hallucination Detection in Neural Machine Translation through
  Simple Detector Aggregation
Anas Himmi Guillaume Staerman Marine Picot Pierre Colombo Nuno M. Guerreiro
Hallucinated translations pose significant threats and safety concerns when
it comes to the 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.