Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)
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| Challenge: | Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks. |
| Approach: | They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations. |
| Outcome: | The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings. |
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| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
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| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
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| Challenge: | In multilingual pre-training, multilingual models only learn cross-linguality implicitly from isomorphic spaces formed by overlapping different language spaces due to the lack of explicit cross-linguistic forward pass. |
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| Challenge: | Existing methods to train cross-lingual pre-trained language models have shown great success in cross-linguistic sequence labeling tasks. |
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| Challenge: | Recent studies improve cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. |
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| Challenge: | Existing methods to train multi-lingual sentence embeddings ruins the mono-lingual space. |
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Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning? (2024.lrec-main)
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| Challenge: | Existing models that pretrain for cross-lingual tasks do not improve cross-linguistic learning. |
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Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
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Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac, Pranava Madhyastha, Erkut Erdem, Aykut Erdem, Lucia Specia
| Challenge: | Pre-trained language models have been shown to improve performance in many natural language tasks. |
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