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.

翻译：海量多语言机器翻译模型能够通过单一模型实现大量语言间的翻译，但在低资源和极低资源翻译方向上性能有限。通过高资源语言进行枢纽翻译仍是处理低资源方向的有效策略，本文重新探讨了通过多语言实现枢纽翻译的方法。以往研究采用多个路径概率分布的简单平均，但我们发现其效果甚至劣于单一枢纽方法，且会加剧幻觉问题——因为同一幻觉内容在不同路径中可能同样具有高概率。作为替代方案，我们提出最大集成策略（MaxEns），该组合策略倾向于选择置信度最高的预测结果，其假设是置信度高的预测更不易产生幻觉。我们在FLORES基准上针对20个低资源语言方向评估了不同策略，结果表明：相较于直接翻译和平均方法，MaxEns在提升低资源语言翻译质量的同时减少了译文中的幻觉现象。然而平均而言，多枢纽策略仍落后于以英语为单一枢纽语言的方法，这引发了如何针对特定翻译方向确定最优枢纽策略的思考。