Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation
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.
Paper Type: Short
Research Area: Machine Translation
Research Area Keywords: Machine Translation
Contribution Types: Model analysis & interpretability, Approaches to low-resource settings, Approaches low compute settings-efficiency
Languages Studied: en,de
Submission Number: 271
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