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How Transliterations Improve Crosslingual Alignment

Paper · 2025 · Yihong Liu, Mingyang Wang, Amir Hossein Kargaran et al.

Summary

Multilingual pretrained language models (mPLMs) transfer knowledge less effectively between languages written in different scripts. Earlier work found that post-aligning such models on text paired with its own transliteration improves crosslingual transfer, even without parallel data. Why this works had not been explained. The authors study it on two related language pairs that use different scripts: Polish–Ukrainian and Hindi–Urdu. They train several model variants that differ in two ways: whether Latin-script transliterated data is added, and which auxiliary objective is used, such as transliteration contrastive modeling (TCM) or transliteration language modeling (TLM). Alignment is measured directly with four kinds of sentence-representation similarity, tracked throughout training and tied to sentence retrieval. Adding transliterations alone raises every similarity, including for random sentence pairs, so it does not produce real alignment. The auxiliary objectives, especially the contrastive one, teach the model to separate true translation pairs from random ones, with transliterations acting as a bridge. However, stronger alignment does not consistently improve downstream results. This is a useful caution for work on languages written in non-Latin scripts, such as Ukrainian.

Key points

  • The paper uses a "weak alignment" definition: a sentence must be closer to its translation than to any other candidate. What matters is relative similarity, not absolute similarity, which makes top-k sentence retrieval a direct test of alignment.
  • The proposed mechanism works in three steps:
    • Each language in its native script (e.g., hin_Deva, urd_Arab) is aligned with its own Latin transliteration.
    • The Latin versions share more vocabulary with each other.
    • As a result, the original-script texts become better aligned with each other, and this effect is strongest with TCM.
  • Five model variants (Model-1 to Model-5) were fine-tuned and evaluated on SIB200, NER and POS, with results averaged over three random seeds.
  • The downstream picture is mixed:
    • NER and POS scores stay nearly flat across all models.
    • SIB200 improves for pol → ukr and urd → hin but drops slightly for ukr → pol and hin → urd.
    • Auxiliary objectives can hurt same-language performance, for example SIB200 when training and testing on Hindi.
  • The authors note three limitations: only two language pairs, sentence-level analysis only, and no token-level alignment objectives. Code is available at github.com/cisnlp/Transliteration-PPA.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Ludwig-Maximilians-Universität MünchenMunich · Germany107 works · 56,478 citations🔬Centre National de la Recherche ScientifiqueParis · France168 works · 62,369 citations🎓Technical University of MunichMunich · Germany55 works · 48,474 citations🎓Sorbonne UniversitéParis · France32 works · 7,270 citations🎓Munich Center for Machine LearningMunich · Germany57 works · 1,094 citations🔬Institut Systèmes Intelligents et de RobotiqueParis · France8 works · 206 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 828 works

Cite

Cite as Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon and Hinrich Schütze. 2025. How Transliterations Improve Crosslingual Alignment. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{liu2025how,
  title = {{How Transliterations Improve Crosslingual Alignment}},
  author = {Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon and Hinrich Schütze},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.165/}
}

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