@inproceedings{shi-etal-2026-cemt,
title = "{CEMT}:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning",
author = "Shi, Lingling and
Jin, Haoyu and
Fang, Ruiyu and
Song, Shuangyong and
Su, Jinsong and
Li, Yongxiang and
Li, Xuelong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1882/",
pages = "37755--37781",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models have shown strong performance in Machine Translation, yet they often suffer from paraphrasing errors, omissions, or hallucinations when the input contains translation-specific elements (e.g., URLs, slang, and idioms) that require strict preservation or controlled transformation, undermining the reliability of critical details.We propose CEMT, a Controllable Element-Oriented Machine Translation framework inspired by the analysis{--}strategy{--}generation paradigm in human translation. CEMT first employs an Element Detection Module to identify translation-specific elements, and then introduces a Translation Module that decomposes the translation process into linguistically grounded analysis, strategy formulation, and final generation, thereby guiding the reliable translation of these elements. We further introduce a CoT Judge model during training that provides step-wise supervision over the accuracy and consistency of the translation process.On the WMT23/24 Chinese{--}English benchmarks, CEMT improves performance over existing Machine Translation models while significantly reducing element-level constraint violations."
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%0 Conference Proceedings
%T CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning
%A Shi, Lingling
%A Jin, Haoyu
%A Fang, Ruiyu
%A Song, Shuangyong
%A Su, Jinsong
%A Li, Yongxiang
%A Li, Xuelong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shi-etal-2026-cemt
%X Large Language Models have shown strong performance in Machine Translation, yet they often suffer from paraphrasing errors, omissions, or hallucinations when the input contains translation-specific elements (e.g., URLs, slang, and idioms) that require strict preservation or controlled transformation, undermining the reliability of critical details.We propose CEMT, a Controllable Element-Oriented Machine Translation framework inspired by the analysis–strategy–generation paradigm in human translation. CEMT first employs an Element Detection Module to identify translation-specific elements, and then introduces a Translation Module that decomposes the translation process into linguistically grounded analysis, strategy formulation, and final generation, thereby guiding the reliable translation of these elements. We further introduce a CoT Judge model during training that provides step-wise supervision over the accuracy and consistency of the translation process.On the WMT23/24 Chinese–English benchmarks, CEMT improves performance over existing Machine Translation models while significantly reducing element-level constraint violations.
%U https://aclanthology.org/2026.findings-acl.1882/
%P 37755-37781
Markdown (Informal)
[CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning](https://aclanthology.org/2026.findings-acl.1882/) (Shi et al., Findings 2026)
ACL
- Lingling Shi, Haoyu Jin, Ruiyu Fang, Shuangyong Song, Jinsong Su, Yongxiang Li, and Xuelong Li. 2026. CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 37755–37781, San Diego, California, United States. Association for Computational Linguistics.