| Challenge: | Existing multilingual pre-trained language models allow to adapt to target languages with only few labeled examples. |
| Approach: | They propose a simple cross-lingual sub-network tuning method that detects the most essential sub-netzwork for each target language and updates it during fine-tuning. |
| Outcome: | The proposed method improves on three multi-lingual tasks involving 37 different languages. |
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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
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Consistency Regularization for Cross-Lingual Fine-Tuning (2021.acl-long)
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Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei
| Challenge: | Experimental results show that consistency regularization improves cross-lingual fine-tuning . pre-trained cross-linguistic models can transfer task-specific supervision from one language to the other . |
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MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (D19-1)
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| Challenge: | Pretrained language models require unlabelled data for training, while cross-lingual models underperform on low-resource languages. |
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Effective Fine-Tuning Methods for Cross-lingual Adaptation (2021.emnlp-main)
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| Challenge: | Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks. |
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Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Existing methods to make multilingual systems expensive and tedious introduce pipeline of errors. |
| Approach: | They propose to use pre-trained multilingual models to enhance the transfer learning process by intermediate fine-tuning of pretrained multi-lingual models. |
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Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (2023.acl-long)
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| Challenge: | Existing research shows that a multilingual pre-trained language model fine-tuned with one (source) language performs well on downstream tasks for non-source languages . However, there is a clear performance gap between the source and non-sourced languages - this gap can be reduced by reducing forgetting. |
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Exploring Intrinsic Language-specific Subspaces in Fine-tuning Multilingual Neural Machine Translation (2024.emnlp-main)
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| Challenge: | Multilingual neural machine translation models support fine-tuning hundreds of languages simultaneously. |
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X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity (2023.findings-emnlp)
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| Challenge: | Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance when evaluated in non-English languages. |
| Approach: | They propose to use sub-network similarity between two languages as a proxy for XLT prediction. |
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Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning (2021.emnlp-main)
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| Challenge: | Recent pretrained language models extend from millions to billions of parameters. |
| Approach: | They propose a technique which forwards on a whole network while backwarding on resetting the gradients of the non-child network during the backward process. |
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Don’t Stop Fine-Tuning: On Training Regimes for Few-Shot Cross-Lingual Transfer with Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Recent work highlights the fallacies of zero-shot cross-lingual transfer with large multilingual models. |
| Approach: | They propose to replace sequential fine-tuning with joint fine-uning on source and target language instances. |
| Outcome: | The proposed techniques yield improved and more stable FS-XLT across the board. |