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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. - : arXiv, 2019 BASE Show 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; Grefenstette, Edward; Ling, Wang. - : arXiv, 2016 Abstract: We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences. In contrast with prior work on tree-structured models in which the trees are either provided as input or predicted using supervision from explicit treebank annotations, the tree structures in this work are optimized to improve performance on a downstream task. Experiments demonstrate the benefit of learning task-specific composition orders, outperforming both sequential encoders and recursive encoders based on treebank annotations. We analyze the induced trees and show that while they discover some linguistically intuitive structures (e.g., noun phrases, simple verb phrases), they are different than conventional English syntactic structures. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1611.09100 https://arxiv.org/abs/1611.09100 BASE Hide 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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