A B ayesian Optimization Approach to Machine Translation Reranking (2025)

Abstract: Reranking, or scoring a list of prediction candidates from a machine translation system with an external scoring model and returning the highest-scoring candidate, remains a simple and effective method for improving prediction quality. However, reranking with high quality scoring models can add substantial computational cost to the translation pipeline, which we address in this work by framing list reranking as a Bayesian optimization (BayesOpt) problem over the candidate list, where unknown scores are modeled with a Gaussian process. This algorithm scores candidates iteratively, choosing next candidates by balancing between exploration, choosing to score those that differ from candidates already scored, and exploitation, choosing to score those that resemble high-scoring candidates.This procedure finds high-scoring candidates while scoring only a fraction of the candidates list; given candidate lists of 200 random samples (before deduplication), our method achieves the same CometKiwi score using only 70 scoring evaluations on average compared to scoring a random subset of 180 candidates. We also propose multi-fidelity BayesOpt for list reranking, where scores obtained from a noisier but cheaper proxy scoring model are incorporated into the search process. We show that well-trained distilled proxy scorers can further improve the performance of BayesOpt.

## introduction reranking is a framework for prediction where probabilistic generator model produces a list of candidates, and a separate evaluator or scoring model produces scores for each of the candidates which are use to determine the final prediction. reranking has a long history in natural language processing for sequential prediction problems such as dependency parsing @xcite 0 paper code: github.com/juliusc/bayesopt_reranking figure 1 : a machine translation system generates candidates aa, bb, cc, dd, and ee. the goal of bayesopt is to find the highest scoring candidate with fewer scoring calls. an acquisition function selects the next candidate to score repeatedly until budget is reached, and the candidate with the highest score so far is returned. @xcite and language modeling problems such as summarization @xcite and machine translation (mt; @xcite . the quality of models for automatic mt evaluation has surged in recent years due to innovations in neural network architecture @xcite @xcite as well as the abundance of training data @xcite . these evaluation models are often repurposed for reranking to further improve the performance of an mt system. for instance, in the wmt 2024 shared task @xcite , 5 out of 19 systems, including the overall best submission @xcite use reranking with comet models @xcite and/or minimum bayes risk decoding (mbr; @xcite , which can be interpreted as a form of reranking. prior to the application of automatic evaluation metrics to reranking, other scoring methods have been proposed, including discriminatively trained classifiers @xcite and noisy channel decoding @xcite . so, while lms for mt generation for greatly improved in recent years, scoring models have seen a commensurate increase in quality @xcite , and thus reranking remains relevant method for improving translation quality. however, the scoring models have also grown dramatically in size, increasing the computational requirements for reranking. in this work, we address the computational cost of reranking by framing it as a search problem over the list of candidates. the goal of search in this setting is to find high-scoring candidates in a small number of steps, thereby avoiding the cost of scoring the full list. our proposed algorithm uses gaussian processes to model uncertainty about unseen scores and bayesian optimization (bayesopt; @xcite to choose which candidates to score next. gps are flexible priors over functions which are able to model the complex and nonlinear relationship between each candidate and its score. gps make very few assumptions about the distribution and base their predictions are mostly on observed points, which enables them to easily adapt to different candidate lists across translation instances. bayesopt is a sequential black-box optimization method that uses the posterior mean and variance of unobserved data points to decide which points to evaluate next. we apply bayesopt and gps (bayesopt+gp) to mt list reranking in a straightforward manner and show that it obtains close to the maximum achievable score with only a fraction of score evaluations. for example, the maximal obtainable score across 200 randomly sampled candidates on our test set is 0.8216 cometkiwi; our method achieves 0.8210 with 70 score evaluations on average, while scoring 70 random candidates attains 0.8149, a difference of 0.0061 which is likely to be human-detectable according to @xcite . we also propose a number of search-based baselines which outperform random selection, all of which are outperformed by bayesopt+gp. then, building upon previous works that use a faster but noisier proxy scoring function to prune the candidate list @xcite , we propose a multi-fidelity extension to bayesopt which incorporates proxy scores to improve estimation. this is related in motivation to coarse-to-fine methods @xcite and model cascading @xcite , where the use of a faster proxy model reduces the use of the main model. in our multi-fidelity experiments, we find that smaller proxy scoring models distilled from the main model can assist bayesopt+gp in finding high-scoring candidates earlier. ## experiments for bayesopt experiments, we grid search for the optimal value of rbf bandwidth parameter w on the entire validation set, setting scoring budget @xmath0. while it is possible to optimize it for every unique combination of language pair, n, k, proxy scoring function, and β, we find that the results are not statistically significantly different within a range of settings. for simplicity, and to demonstrate the robustness of our methods, we use the same w for all experiments. in all experiments, we use α = 10 initial randomly scored candidates. we set @xmath1.2 and 4.3 to demonstrate the effectiveness of bayesopt+gp under ideal conditions, but since k can have a large impact on speed, we experiment with varying it in section 4.5. ## conclusion in this work, we formalize mt reranking as a bayesian optimization problem, leveraging the ba-sic observation that similar translations are more likely to have similar quality scores. we also extend the framework to accept observations from proxy scoring functions, which is applicable when the target score is very costly: large qe models, mbr, or human evaluation. in realistic experiments, we show that our methods improve reranking efficiency over strong baselines. we also propose several design choices that make the methods useful in practice; a gp kernel that requires minimal overhead, and effective proxy model training via distillation. we consider our work a first step in applying bayesopt to mt reranking. future directions include integrating bayesopt with candidate generation, alternative acquisition functions, and further exploration of gp kernels for mt.