Massively multilingual machine translation models allow for the translation
of a large number of languages with a single model, but have limited
performance on low- and very-low-resource translation directions. Pivoting via
high-resource languages remains a strong strategy for low-resource directions,
and in this paper we revisit ways of pivoting through multiple languages.
Previous work has used a simple averaging of probability distributions from
multiple paths, but we find that this performs worse than using a single pivot,
and exacerbates the hallucination problem because the same hallucinations can
be probable across different paths. As an alternative, we propose MaxEns, a
combination strategy that is biased towards the most confident predictions,
hypothesising that confident predictions are less prone to be hallucinations.
We evaluate different strategies on the FLORES benchmark for 20 low-resource
language directions, demonstrating that MaxEns improves translation quality for
low-resource languages while reducing hallucination in translations, compared
to both direct translation and an averaging approach. On average, multi-pivot
strategies still lag behind using English as a single pivot language, raising
the question of how to identify the best pivoting strategy for a given
translation direction.

大规模多语言机器翻译模型在一种模型中翻译大量语言，但在低资源和非常低资源的翻译方向上的性能有限。在这篇论文中，我们重新审视通过多个语言进行枢轴翻译的方法。我们提出了 MaxEns 作为一种组合策略，偏向于最自信的预测，以减少翻译中出现的幻觉问题。通过在 FLORES 基准测试中评估不同策略，我们证明 MaxEns 提高低资源语言的翻译质量，同时减少翻译中的幻觉，相对于直接翻译和平均方法。平均而言，多枢轴策略仍然落后于使用英语作为单个枢轴语言，这引发了如何为特定翻译方向确定最佳枢轴策略的问题。