  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. We also propose MaxEns, a novel combination
strategy that makes the output 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.
