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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 – 13 of 13 1 Analogy Training Multilingual Encoders ... Garneau, Nicolas; Hartmann, Mareike; Sandholm, Anders. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 2 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 3 How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ... Rust, Phillip; Pfeiffer, Jonas; Vulić, Ivan. - : arXiv, 2020 BASE Show details 4 UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 5 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ... Pfeiffer, Jonas; Vulic, Ivan; Gurevych, Iryna. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 6 Are All Good Word Vector Spaces Isomorphic? Vulic, Ivan; Ruder, Sebastian; Søgaard, Anders. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 7 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer Vulic, Ivan; Pfeiffer, Jonas; Ruder, Sebastian. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 8 AdapterHub: A Framework for Adapting Transformers Pfeiffer, Jonas; Ruckle, Andreas; Poth, Clifton; Kamath, Aishwarya; Vulic, Ivan; Ruder, Sebastian; Cho, Kyunghyun; Gurevych, Iryna. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP 2020), 2020 Abstract: The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of hundreds of millions, or even billions of parameters. Storing and sharing such large trained models is expensive, slow, and time-consuming, which impedes progress towards more general and versatile NLP methods that learn from and for many tasks. Adapters-small learnt bottleneck layers inserted within each layer of a pre-trained model- ameliorate this issue by avoiding full fine-tuning of the entire model. However, sharing and integrating adapter layers is not straightforward. We propose AdapterHub, a framework that allows dynamic "stiching-in" of pre-trained adapters for different tasks and languages. The framework, built on top of the popular HuggingFace Transformers library, enables extremely easy and quick adaptations of state-of-the-art pre-trained models (e.g., BERT, RoBERTa, XLM-R) across tasks and languages. Downloading, sharing, and training adapters is as seamless as possible using minimal changes to the training scripts and a specialized infrastructure. Our framework enables scalable and easy access to sharing of task-specific models, particularly in low-resource scenarios. AdapterHub includes all recent adapter architectures and can be found at AdapterHub.ml. URL: https://doi.org/10.17863/CAM.62205 https://www.repository.cam.ac.uk/handle/1810/315098 BASE Hide details 9 How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ... Glavas, Goran; Litschko, Robert; Ruder, Sebastian. - : arXiv, 2019 BASE Show details 10 Specializing distributional vectors of all words for lexical entailment Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019 BASE Show details 11 How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions Glavaš, Goran; Litschko, Robert; Ruder, Sebastian. - : Association for Computational Linguistics, 2019 BASE Show details 12 On the Limitations of Unsupervised Bilingual Dictionary Induction ... Søgaard, Anders; Ruder, Sebastian; Vulić, Ivan. - : arXiv, 2018 BASE Show details 13 A Survey Of Cross-lingual Word Embedding Models ... Ruder, Sebastian; Vulić, Ivan; Søgaard, Anders. - : arXiv, 2017 BASE Show details

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