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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 – 16 of 16 1 Delving Deeper into Cross-lingual Visual Question Answering ... Liu, Chen; Pfeiffer, Jonas; Korhonen, Anna. - : arXiv, 2022 BASE Show details 2 IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ... Bugliarello, Emanuele; Liu, Fangyu; Pfeiffer, Jonas. - : arXiv, 2022 BASE Show details 3 Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ... Ribeiro, Leonardo F. R.; Pfeiffer, Jonas; Zhang, Yue. - : arXiv, 2021 BASE Show details 4 xGQA: Cross-Lingual Visual Question Answering ... Pfeiffer, Jonas; Geigle, Gregor; Kamath, Aishwarya. - : arXiv, 2021 BASE Show details 5 Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Iryna; Pfeiffer, Jonas. - : Underline Science Inc., 2021 BASE Show details 6 UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Iryna; ., Sebastian. - : Underline Science Inc., 2021 BASE Show details 7 How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Iryna; ., Sebastian. - : Underline Science Inc., 2021 BASE Show details 8 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 9 How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ... Rust, Phillip; Pfeiffer, Jonas; Vulić, Ivan. - : arXiv, 2020 BASE Show details 10 UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 11 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 12 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 13 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 14 Specialising Distributional Vectors of All Words for Lexical Entailment ... Kamath, Aishwarya; Pfeiffer, Jonas; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 15 Specializing distributional vectors of all words for lexical entailment Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019 BASE Show details 16 A neural autoencoder approach for document ranking and query refinement in pharmacogenomic information retrieval Broscheit, Samuel; Pfeiffer, Jonas; Gemulla, Rainer. - : Association for Computational Linguistics, 2018 BASE Show details

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