UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages (2024.acl-long)
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| Challenge: | Existing pre-trained language models are weak in addressing cross-lingual transfer tasks. |
| Approach: | They propose a method for initializing embeddings and choosing the right vocabulary size for cross-lingual systems. |
| Outcome: | The proposed method improves the F1-Score in several languages . |
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
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| Challenge: | Massively multilingual language models offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks, but there is a profound performance gap between resource-rich and resource-poor target languages. |
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| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
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Unsupervised Cross-lingual Transfer of Word Embedding Spaces (D18-1)
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| Challenge: | Existing methods for cross-lingual word mapping require cross-linguistic supervision, but this is not available for many low resource languages. |
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| Challenge: | Pre-trained multilingual language models are the foundation of many NLP approaches, but are often not well-supported by these models due to small available monolingual corpora. |
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