  Neural machine translation systems estimate probabilities of target sentences
given source sentences, yet these estimates may not align with human
preferences. This work introduces QE-fusion, a method that synthesizes
translations using a quality estimation metric (QE), which correlates better
with human judgments. QE-fusion leverages a pool of candidates sampled from a
model, combining spans from different candidates using a QE metric such as
CometKiwi. We compare QE-fusion against beam search and recent reranking
techniques, such as Minimum Bayes Risk decoding or QE-reranking. Our method
consistently improves translation quality in terms of COMET and BLEURT scores
when applied to large language models (LLMs) used for translation (PolyLM,
XGLM, Llama2, Mistral, ALMA, and Tower) and to multilingual translation models
(NLLB), over five language pairs. Notably, QE-fusion exhibits larger
improvements for LLMs due to their ability to generate diverse outputs. We
demonstrate that our approach generates novel translations in over half of the
cases and consistently outperforms other methods across varying numbers of
candidates (5-200). Furthermore, we empirically establish that QE-fusion scales
linearly with the number of candidates in the pool.
