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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 – 10 of 10 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 On the Cross-lingual Transferability of Monolingual Representations ... Artetxe, Mikel; Ruder, Sebastian; Yogatama, Dani. - : arXiv, 2019 Abstract: State-of-the-art unsupervised multilingual models (e.g., multilingual BERT) have been shown to generalize in a zero-shot cross-lingual setting. This generalization ability has been attributed to the use of a shared subword vocabulary and joint training across multiple languages giving rise to deep multilingual abstractions. We evaluate this hypothesis by designing an alternative approach that transfers a monolingual model to new languages at the lexical level. More concretely, we first train a transformer-based masked language model on one language, and transfer it to a new language by learning a new embedding matrix with the same masked language modeling objective, freezing parameters of all other layers. This approach does not rely on a shared vocabulary or joint training. However, we show that it is competitive with multilingual BERT on standard cross-lingual classification benchmarks and on a new Cross-lingual Question Answering Dataset (XQuAD). Our results contradict common beliefs of the basis of the ... : ACL 2020 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.1910.11856 https://arxiv.org/abs/1910.11856 BASE Hide details 4 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Manaal Faruqui; Dyer, Chris. - : Carnegie Mellon University, 2015 BASE Show details 5 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Manaal Faruqui; Dyer, Chris. - : Carnegie Mellon University, 2015 BASE Show details 6 Linguistic Structured Sparsity in Text Categorization ... Yogatama, Dani; Smith, Noah A.. - : Carnegie Mellon University, 2014 BASE Show details 7 Linguistic Structured Sparsity in Text Categorization ... Yogatama, Dani; Smith, Noah A.. - : Carnegie Mellon University, 2014 BASE Show details 8 Predicting a Scientific Community’s Response to an Article ... Yogatama, Dani; Heliman, Michael; O'Connor, Brendan. - : Carnegie Mellon University, 2011 BASE Show details 9 Predicting a Scientific Community’s Response to an Article ... Yogatama, Dani; Heliman, Michael; O'Connor, Brendan. - : Carnegie Mellon University, 2011 BASE Show details 10 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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