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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 38 1 XTREME-S: Evaluating Cross-lingual Speech Representations ... Conneau, Alexis; Bapna, Ankur; Zhang, Yu. - : arXiv, 2022 BASE Show details 2 One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia ... Aji, Alham Fikri; Winata, Genta Indra; Koto, Fajri. - : arXiv, 2022 BASE Show details 3 Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ... Wang, Xinyi; Ruder, Sebastian; Neubig, Graham. - : arXiv, 2022 BASE Show details 4 MasakhaNER: Named entity recognition for African languages Adelani, David,; Abbott, Jade; Neubig, Graham... In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021) BASE Show details 5 Charformer: Fast Character Transformers via Gradient-based Subword Tokenization ... Tay, Yi; Tran, Vinh Q.; Ruder, Sebastian. - : arXiv, 2021 BASE Show details 6 Multi-view Subword Regularization ... Wang, Xinyi; Ruder, Sebastian; Neubig, Graham. - : arXiv, 2021 BASE Show details 7 XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ... Ruder, Sebastian; Constant, Noah; Botha, Jan. - : arXiv, 2021 BASE Show details 8 Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ... Wang, Xinyi; Tsvetkov, Yulia; Ruder, Sebastian. - : arXiv, 2021 BASE Show details 9 Analogy Training Multilingual Encoders ... Garneau, Nicolas; Hartmann, Mareike; Sandholm, Anders. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 10 XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ruder, Sebastian. - : Underline Science Inc., 2021 BASE Show details 11 A Call for More Rigor in Unsupervised Cross-lingual Learning ... Artetxe, Mikel; Ruder, Sebastian; Yogatama, Dani. - : arXiv, 2020 BASE Show details 12 Rethinking embedding coupling in pre-trained language models ... Chung, Hyung Won; Févry, Thibault; Tsai, Henry. - : arXiv, 2020 BASE Show details 13 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 14 How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ... Rust, Phillip; Pfeiffer, Jonas; Vulić, Ivan. - : arXiv, 2020 BASE Show details 15 UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 16 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ... Pfeiffer, Jonas; Vulic, Ivan; Gurevych, Iryna; Ruder, Sebastian. - : Apollo - University of Cambridge Repository, 2020 Abstract: The main goal behind state-of-the-art pretrained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pretraining. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pretrained multilingual model to a new language. MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. ... URL: https://dx.doi.org/10.17863/cam.62211 https://www.repository.cam.ac.uk/handle/1810/315104 BASE Hide details 17 Morphologically Aware Word-Level Translation ... Czarnowska, Paula; Ruder, Sebastian; Cotterell, Ryan. - : arXiv, 2020 BASE Show details 18 Morphologically Aware Word-Level Translation Czarnowska, Paula; Copestake, Ann; Ruder, Sebastian... In: Proceedings of the 28th International Conference on Computational Linguistics (2020) BASE Show details 19 Morphologically Aware Word-Level Translation ... The 28th International Conference on Computational Linguistics 2020; Copestake, Ann; Cotterell, Ryan. - : Underline Science Inc., 2020 BASE Show details 20 XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization ... Hu, Junjie; Ruder, Sebastian; Siddhant, Aditya. - : arXiv, 2020 BASE Show details Page: 1 2

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