Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation

arXiv cs.CL / 3/27/2026

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Key Points

The paper argues that context-aware machine translation (document-level signals) often fails to reliably beat sentence-level MT because the usefulness of context varies unevenly across sentences.
It introduces Cross-Preference Learning (CPL), a preference-based training framework that explicitly models complementary strengths between sentence-level and context-aware MT.
CPL incorporates both intra-condition and cross-condition preferences into a single optimization objective, providing supervision on when and how contextual information improves translation quality.
Experiments on several public context-aware MT benchmarks using multiple models (Qwen3-4B, Qwen3-8B, Llama-3-8B) show consistent gains in translation quality and robustness.
The improvements reportedly come without any architectural changes, suggesting CPL is a training objective upgrade that can generalize across model types.

Abstract

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.