@inproceedings{li-etal-2026-cross-preference,
title = "Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation",
author = "Li, Ying and
Lyu, Xinglin and
Li, Junhui and
Yang, Jinlong and
Shang, Hengchao and
Zhang, Min and
Tao, Shimin and
Wei, Daimeng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2543/",
pages = "57823--57844",
ISBN = "979-8-89176-459-0",
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, providing explicit supervision to exploit informative context while remaining robust to uninformative context. We validate the proposed approach on several public context-aware MT tasks using multiple models, including Qwen3-4B, Qwen3-8B, and Llama-3-8B-Instruct. Experimental results demonstrate consistent improvements in translation quality and robustness across both input conditions, achieved without any architectural modifications."
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<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, providing explicit supervision to exploit informative context while remaining robust to uninformative context. We validate the proposed approach on several public context-aware MT tasks using multiple models, including Qwen3-4B, Qwen3-8B, and Llama-3-8B-Instruct. Experimental results demonstrate consistent improvements in translation quality and robustness across both input conditions, achieved without any architectural modifications.</abstract>
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%0 Conference Proceedings
%T Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation
%A Li, Ying
%A Lyu, Xinglin
%A Li, Junhui
%A Yang, Jinlong
%A Shang, Hengchao
%A Zhang, Min
%A Tao, Shimin
%A Wei, Daimeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-cross-preference
%X 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, providing explicit supervision to exploit informative context while remaining robust to uninformative context. We validate the proposed approach on several public context-aware MT tasks using multiple models, including Qwen3-4B, Qwen3-8B, and Llama-3-8B-Instruct. Experimental results demonstrate consistent improvements in translation quality and robustness across both input conditions, achieved without any architectural modifications.
%U https://aclanthology.org/2026.emnlp-main.2543/
%P 57823-57844
Markdown (Informal)
[Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation](https://aclanthology.org/2026.emnlp-main.2543/) (Li et al., EMNLP 2026)
ACL
- Ying Li, Xinglin Lyu, Junhui Li, Jinlong Yang, Hengchao Shang, Min Zhang, Shimin Tao, and Daimeng Wei. 2026. Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57823–57844, Budapest, Hungary. Association for Computational Linguistics.