TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated Data (2025.coling-main)
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| Challenge: | Existing mPLMs that handle non-transliterated data are not sufficient to train crosslingual models. |
| Approach: | They propose a framework to transliterate related languages into a common script by exploiting existing mPLMs and their tokenizer without any training. |
| Outcome: | The proposed framework can create strong baselines for data that is transliterated into a common script by exploiting an existing mPLM and its tokenizer without any training. |
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| Challenge: | Recent mPLMs have shown impressive performance on crosslingual transfer tasks . however, the performance is often hindered when a lowresource target language is written in a different script than the high-resource source language. |
| Approach: | They propose a transliteration-based method to improve cross-lingual alignment between languages using diverse scripts. |
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TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models (2024.acl-long)
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| Challenge: | The world’s more than 7000 languages are written in at least 293 scripts, which poses a difficulty for multilingual pretrained language models in learning crosslingual knowledge through lexical overlap. |
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ScriptMix: Mixing Scripts for Low-resource Language Parsing (2024.naacl-long)
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| Challenge: | Existing work has considered transliteration and vocabulary augmentation, but the consideration of combining the two has been lacking. |
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Romanization-based Large-scale Adaptation of Multilingual Language Models (2023.findings-emnlp)
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| Challenge: | Large multilingual pretrained language models are limited by their vocabulary size and parameter budget. |
| Approach: | They explore the potential of leveraging transliteration on a massive scale to improve performance for multilingual pretrained language models. |
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LangSAMP: Language-Script Aware Multilingual Pretraining (2025.acl-long)
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| Challenge: | Recent multilingual pretrained language models often avoid using language embeddings, which places a significant burden on token representations to encode all language-specific information. |
| Approach: | They propose a method that incorporates both language and script embeddings into the output of Transformer blocks before passing the final representations to the language modeling head for prediction. |
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How Transliterations Improve Crosslingual Alignment (2025.coling-main)
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Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze
| Challenge: | Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved. |
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WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models (2022.naacl-main)
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| Challenge: | Existing methods to train large pretrained language models require more computational resources and are expensive to train in other languages. |
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Enhancing Cross-Lingual Transfer through Reversible Transliteration: A Huffman-Based Approach for Low-Resource Languages (2025.acl-long)
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| Challenge: | Large language models demonstrate cross-lingual transfer capabilities, but these capabilities often fail to extend to low-resource languages, especially those utilizing non-Latin scripts. |
| Approach: | They propose to combine character transliteration with Huffman coding to create a complete transliterations framework that can be extended to other low-resource languages. |
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Take a Closer Look at Multilinguality! Improve Multilingual Pre-Training Using Monolingual Corpora Only (2023.findings-emnlp)
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| Challenge: | Recent studies have demonstrated remarkable cross-lingual capability of pre-trained language models . however, semantic alignments may be the reason behind such capability but remain under-explored. |
| Approach: | They propose token-level and semantic-level code-switched masked language modeling to improve cross-lingual interactions over mono-mPLMs without parallel sentences. |
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OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining (2024.findings-naacl)
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| Challenge: | Existing methods to pretrain multilingual models are limited by the number of embedding parameters and the complexity of the model. |
| Approach: | They propose a framework that initializes the embeddings of unseen subwords and can adapt a model to multiple languages. |
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