BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer (2022.naacl-main)
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| Challenge: | Massively multilingual Transformers (MMTs) have dominated research in multilingual NLP and cross-lingual transfer recently. |
| Approach: | They propose to learn bilingual language pair adapters (BAs) when the goal is to optimize performance for a particular source-target transfer direction. |
| Outcome: | The proposed framework improves performance in three standard downstream tasks and for the majority of low-resource languages. |
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| Challenge: | Existing frameworks for (zero-shot) cross-lingual transfer employ separate language and task adapters which can be arbitrarily combined to perform any task to any target language. |
| Approach: | They propose to fine-tune 'target language-ready' adapters to the target language to achieve better transfer performance without sacrificing the modularity of MAD-X. |
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| Challenge: | Current deep pretrained models lack capacity to represent all languages . limited capacity is an issue even for high-resource languages where models are not included in training data at all. |
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Zero-shot Cross-lingual Transfer With Learned Projections Using Unlabeled Target-Language Data (2023.acl-short)
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| Challenge: | Zero-shot cross-lingual transfer is enabled by pairing the language adapter in the target language with an appropriate task adapter within a source language. |
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A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
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FAD-X: Fusing Adapters for Cross-lingual Transfer to Low-Resource Languages (2022.aacl-short)
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| Challenge: | Adapter-based tuning is a technique that selectively updates language-specific parameters to adapt to a new language, rather than fine-tuning all shared weights. |
| Approach: | They propose to add light-weight adapters to multilingual pretrained language models (mPLMs) and add language-specific parameters to adapt to a new language. |
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MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer (2021.findings-emnlp)
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Alan Ansell, Edoardo Maria Ponti, Jonas Pfeiffer, Sebastian Ruder, Goran Glavaš, Ivan Vulić, Anna Korhonen
| Challenge: | Massively multilingual transformers (MMTs) have benefited from additional training of language-specific adapters, but this approach is not viable for the vast majority of languages due to limitations in their corpus size or compute budgets. |
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Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual generalisability of large pre-trained models use English training data and test data in unseen languages. |
| Approach: | They propose to use multilingual pre-trained models to model cross-lingual transfer in a selection of target languages. |
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Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
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ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters (2023.emnlp-main)
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| Challenge: | Existing approaches to zero-shot cross-lingual transfer have focused on training with adapters of a single source and testing either with the target LA or LA of another related language. |
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Towards Zero-Shot Multilingual Transfer for Code-Switched Responses (2023.acl-long)
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| Challenge: | Recent task-oriented dialog systems have had great success building English-based personal assistants, but extending these systems to a global audience may take tremendous efforts. |
| Approach: | They propose a framework that allows for efficient transfer by learning task-specific representations and encapsulating source and target language representations. |
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