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    • Kamath, Aishwarya (4)
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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 – 4 of 4 1 xGQA: Cross-Lingual Visual Question Answering ... Pfeiffer, Jonas; Geigle, Gregor; Kamath, Aishwarya. - : arXiv, 2021 BASE Show details 2 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 3 Specialising Distributional Vectors of All Words for Lexical Entailment ... Kamath, Aishwarya; Pfeiffer, Jonas; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 4 Specializing distributional vectors of all words for lexical entailment Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019 BASE Show details

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