@inproceedings{yang-etal-2025-enanchored,
title = "{E}n{A}nchored-{X}2{X}: {E}nglish-Anchored Optimization for Many-to-Many Translation",
author = "Yang, Sen and
Bao, Yu and
Lu, Yu and
Chen, Jiajun and
Huang, Shujian and
Cheng, Shanbo",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1081/",
doi = "10.18653/v1/2025.emnlp-main.1081",
pages = "21304--21317",
ISBN = "979-8-89176-332-6",
abstract = "Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models' established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of high-quality x2x training data. Combined with preference-based optimization, our method achieves significant improvement across 72 x2x directions for widely used LLMs, while generalizing to enhance en2x performance. The results demonstrate that strategic exploitation of English-centric strengths can bootstrap comprehensive multilingual translation capabilities in LLMs."
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<abstract>Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of high-quality x2x training data. Combined with preference-based optimization, our method achieves significant improvement across 72 x2x directions for widely used LLMs, while generalizing to enhance en2x performance. The results demonstrate that strategic exploitation of English-centric strengths can bootstrap comprehensive multilingual translation capabilities in LLMs.</abstract>
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%0 Conference Proceedings
%T EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
%A Yang, Sen
%A Bao, Yu
%A Lu, Yu
%A Chen, Jiajun
%A Huang, Shujian
%A Cheng, Shanbo
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F yang-etal-2025-enanchored
%X Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of high-quality x2x training data. Combined with preference-based optimization, our method achieves significant improvement across 72 x2x directions for widely used LLMs, while generalizing to enhance en2x performance. The results demonstrate that strategic exploitation of English-centric strengths can bootstrap comprehensive multilingual translation capabilities in LLMs.
%R 10.18653/v1/2025.emnlp-main.1081
%U https://aclanthology.org/2025.emnlp-main.1081/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1081
%P 21304-21317
Markdown (Informal)
[EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation](https://aclanthology.org/2025.emnlp-main.1081/) (Yang et al., EMNLP 2025)
ACL