@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://preview.aclanthology.org/ingest-acl/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."
}