Neural machine translation (NMT) has become the de-facto standard in
real-world machine translation applications. However, NMT models can
unpredictably produce severely pathological translations, known as
hallucinations, that seriously undermine user trust. It becomes thus crucial to
implement effective preventive strategies to guarantee their proper
functioning. In this paper, we address the problem of hallucination detection
in NMT by following a simple intuition: as hallucinations are detached from the
source content, they exhibit encoder-decoder attention patterns that are
statistically different from those of good quality translations. We frame this
problem with an optimal transport formulation and propose a fully unsupervised,
plug-in detector that can be used with any attention-based NMT model.
Experimental results show that our detector not only outperforms all previous
model-based detectors, but is also competitive with detectors that employ large
models trained on millions of samples.

本文讨论如何通过优化传输和一个完全无监督的插件来检测 NMT 中的幻觉，并表明该检测器不仅优于所有先前的基于模型的检测器，而且与训练在数百万个样本上的大型模型的检测器有竞争力。