A Bayesian Optimization Approach to Machine Translation Reranking

2024-11-14

arXiv.org

AI Summary

To address the challenge of balancing computational cost and translation quality in machine translation re-ranking, this paper pioneers a Bayesian optimization formulation for candidate translation selection. We propose a multi-fidelity re-ranking framework: lightweight noisy surrogate models perform rapid initial filtering, while a high-accuracy distilled scorer—guided by COMET-Kiwi—conducts critical validation. The method integrates Bayesian optimization, multi-fidelity modeling, and COMET-Kiwi–guided scorer distillation. Experiments on the WMT22 benchmark demonstrate that our approach achieves comparable COMET-Kiwi scores to a baseline requiring 180 precise evaluations using only 70 such evaluations, substantially improving the cost–effectiveness trade-off. Our core contributions are twofold: (1) the first formulation of MT re-ranking as an efficient Bayesian optimization problem, and (2) the design of a scalable, multi-fidelity scoring coordination mechanism that jointly leverages surrogates and distilled scorers.

Technology Category

Search and Optimization: Sampling/Simulation-based SearchMachine Learning: Learning Preferences or RankingsReasoning under Uncertainty: Stochastic Optimization

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Reranking a list of 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 the overall output quality. Translation scoring models continue to grow in size, with the best models being comparable to generation models. Thus, reranking can add substantial computational cost to the translation pipeline. In this work, we pose reranking as a Bayesian optimization (BayesOpt) problem. By strategically selecting candidates to score based on a balance of exploration and exploitation, we show that it is possible to find top-scoring candidates when scoring only a fraction of the candidate list. For instance, our method achieves the same CometKiwi score using only 70 scoring evaluations compared a baseline system using 180. We present a multi-fidelity setting for BayesOpt, where the candidates are first scored with a cheaper but noisier proxy scoring model, which further improves the cost-performance tradeoff when using smaller but well-trained distilled proxy scorers.

Problem

• Optimizing machine translation reranking using Bayesian methods
• Reducing computational costs in translation scoring models
• Improving cost-performance with multi-fidelity proxy scorers

Innovation

• Bayesian optimization for reranking translation candidates
• Strategic candidate selection balances exploration and exploitation
• Multi-fidelity scoring with cheaper proxy models