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. |
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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. |
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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. |
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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. |
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