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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 Hits 1 – 3 of 3 1 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 2 How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ... Rust, Phillip; Pfeiffer, Jonas; Vulić, Ivan. - : arXiv, 2020 BASE Show details 3 UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna; Ruder, Sebastian. - : arXiv, 2020 Abstract: Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity and large differences in pretraining data sizes, there is a profound performance gap between resource-rich and resource-poor target languages. The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining. In this work, we propose a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts. Relying on matrix factorization, our methods capitalize on the existing latent knowledge about multiple languages already available in the pretrained model's embedding matrix. Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called ... : EMNLP 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2012.15562 https://dx.doi.org/10.48550/arxiv.2012.15562 BASE Hide details

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