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

• URL: http://arxiv.org/abs/2603.25183v1
• Date: Thu, 26 Mar 2026 08:52:17 GMT
• Title: Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation
• Abstract summary: We propose a preference-based training framework that explicitly captures the complementary benefits of sentence-level and context-aware MT.<n>We validate the proposed approach on several public context-aware MT tasks using multiple models, including Qwen3-4B, Qwen3-8B, and Llama-3-8B.
• Score: 33.915728960928774
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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