Papers by Chunlan Ma
How Transliterations Improve Crosslingual Alignment (2025.coling-main)
Copied to clipboard
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. |
| Approach: | They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data. |
| Outcome: | The proposed approach improves crosslingual alignment even for random sentences. |
TransMI: A Framework to Create Strong Baselines from Multilingual Pretrained Language Models for Transliterated Data (2025.coling-main)
Copied to clipboard
| 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. |
Taxi1500: A Dataset for Multilingual Text Classification in 1500 Languages (2025.naacl-short)
Copied to clipboard
| Challenge: | a large-scale text classification dataset encompassing 1504 languages is needed to address this gap . low-resource languages are often overlooked due to the scarcity of evaluation datasets. |
| Approach: | They propose to use translations of the Bible to construct a large-scale text classification dataset that covers 1504 languages and annotate them using crowdsourcing. |
| Outcome: | The proposed dataset covers 1504 languages and is available to the public. |
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models (2024.acl-long)
Copied to clipboard
| 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. |
| Approach: | They propose a framework that optimizes the Transliteration Contrastive Modeling objective to fine-tune an mPLM by contrasting sentences in its training data and transliterations in a unified script. |
| Outcome: | The proposed model outperforms Glot500-m on zero-shot crosslingual transfer tasks while retaining uniformity across scripts. |
LangSAMP: Language-Script Aware Multilingual Pretraining (2025.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed method outperforms the baseline model in zero-shot crosslingual transfer across diverse downstream tasks. |
Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages (2023.acl-long)
Copied to clipboard
Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, Hinrich Schütze
| Challenge: | Lack of LLMs supporting low-resource languages is a serious impediment to bringing NLP to all of the world. |
| Approach: | They create a model that scales LLMs horizontally and a corpus that covers 511 low-resource languages. |
| Outcome: | The proposed model improves on five diverse tasks across low- and high-resource languages. |