Don't Rank, Combine! Combining Machine Translation Hypotheses Using
  Quality Estimation
Giorgos Vernikos Andrei Popescu-Belis
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 utilizing a quality
estimation metric (QE) that better correlates with human judgments to
synthesize improved translations. QE-fusion leverages a candidate pool sampled
from a model, combining spans from different candidates using QE metrics 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, and Mistral) 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. QE-fusion proves effective in
enhancing LLM-based translation without the need for costly retraining of LLMs.