Context-aware machine translation (MT) leverages document-level information, yet it does not consistently outperform sentence-level MT, as contextual signals are unevenly beneficial across sentences. Existing training objectives do not explicitly model this variability, limiting a model's ability to adaptively exploit context. In this paper, we propose Cross-Preference Learning (CPL), a preference-based training framework that explicitly captures the complementary benefits of sentence-level and context-aware MT. CPL achieves this by integrating both intra- and cross-condition preferences into the preference optimization objective. The introduction of intra- and cross-condition preferences provides explicit supervision on when and how contextual information improves translation quality. We validate the proposed approach on several public context-aware MT tasks using multiple models, including Qwen3-4B, Qwen3-8B, and Llama-3-8B. Experimental results demonstrate consistent improvements in translation quality and robustness across both input conditions, achieved without any architectural modifications.
@article{arxiv.2603.25183,
title = {Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation},
author = {Ying Li and Xinglin Lyu and Junhui Li and Jinlong Yang and Hengchao Shang and Min Zhang and Shimin Tao and Daimeng Wei},
journal= {arXiv preprint arXiv:2603.25183},
year = {2026}
}