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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 – 14 of 14 1 Finetuning Pretrained Transformers into RNNs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Weizhu; Ilharco, Gabriel. - : Underline Science Inc., 2021 BASE Show details 2 A Call for More Rigor in Unsupervised Cross-lingual Learning ... Artetxe, Mikel; Ruder, Sebastian; Yogatama, Dani. - : arXiv, 2020 BASE Show details 3 Learning and Evaluating General Linguistic Intelligence ... Yogatama, Dani; d'Autume, Cyprien de Masson; Connor, Jerome; Kocisky, Tomas; Chrzanowski, Mike; Kong, Lingpeng; Lazaridou, Angeliki; Ling, Wang; Yu, Lei; Dyer, Chris; Blunsom, Phil. - : arXiv, 2019 Abstract: We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly. Using this definition, we analyze state-of-the-art natural language understanding models and conduct an extensive empirical investigation to evaluate them against these criteria through a series of experiments that assess the task-independence of the knowledge being acquired by the learning process. In addition to task performance, we propose a new evaluation metric based on an online encoding of the test data that quantifies how quickly an existing agent (model) learns a new task. Our results show that while the field has made impressive progress in terms of model architectures that generalize to many tasks, these models still require a lot of in-domain training examples (e.g., for fine tuning, training task-specific modules), and are prone to catastrophic forgetting. Moreover, we find that far from solving ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML URL: https://dx.doi.org/10.48550/arxiv.1901.11373 https://arxiv.org/abs/1901.11373 BASE Hide details 4 On the Cross-lingual Transferability of Monolingual Representations ... Artetxe, Mikel; Ruder, Sebastian; Yogatama, Dani. - : arXiv, 2019 BASE Show details 5 Learning to Compose Words into Sentences with Reinforcement Learning ... Yogatama, Dani; Blunsom, Phil; Dyer, Chris. - : arXiv, 2016 BASE Show details 6 Sparse Overcomplete Word Vector Representations ... Faruqui, Manaal; Tsvetkov, Yulia; Yogatama, Dani. - : arXiv, 2015 BASE Show details 7 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Manaal Faruqui; Dyer, Chris. - : Carnegie Mellon University, 2015 BASE Show details 8 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Manaal Faruqui; Dyer, Chris. - : Carnegie Mellon University, 2015 BASE Show details 9 Linguistic Structured Sparsity in Text Categorization ... Yogatama, Dani; Smith, Noah A.. - : Carnegie Mellon University, 2014 BASE Show details 10 Linguistic Structured Sparsity in Text Categorization ... Yogatama, Dani; Smith, Noah A.. - : Carnegie Mellon University, 2014 BASE Show details 11 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Faruqui, Manaal; Dyer, Chris. - : arXiv, 2014 BASE Show details 12 Predicting a Scientific Community’s Response to an Article ... Yogatama, Dani; Heliman, Michael; O'Connor, Brendan. - : Carnegie Mellon University, 2011 BASE Show details 13 Predicting a Scientific Community’s Response to an Article ... Yogatama, Dani; Heliman, Michael; O'Connor, Brendan. - : Carnegie Mellon University, 2011 BASE Show details 14 Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments Gimpel, Kevin; Schneider, Nathan; O'Connor, Brendan... In: DTIC (2010) BASE Show details

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