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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 Page: 1 2 3 Hits 1 – 20 of 46 1 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 2 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models Wilcox, Ethan; Futrell, Richard; Qian, Peng... In: Association for Computational Linguistics (2021) BASE Show details 5 Structural Supervision Improves Learning of Non-Local Grammatical Dependencies Wilcox, Ethan; Qian, Peng; Futrell, Richard... In: Association for Computational Linguistics (2021) BASE Show details 6 Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings ... Saravanakumar, Kailash Karthik; Ballesteros, Miguel; Chandrasekaran, Muthu Kumar. - : arXiv, 2021 BASE Show details 7 A Bag of Tricks for Dialogue Summarization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ballesteros, Miguel; Khalifa, Muhammad. - : Underline Science Inc., 2021 BASE Show details 8 On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representations ... Pérez-Mayos, Laura; Carlini, Roberto; Ballesteros, Miguel. - : arXiv, 2021 BASE Show details 9 How much pretraining data do language models need to learn syntax? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ballesteros, Miguel; Pérez-Mayos, Laura. - : Underline Science Inc., 2021 BASE Show details 10 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 11 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 12 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 13 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 14 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko... In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018) BASE Show details 15 Universal Dependencies 2.3 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 16 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 17 CoNLL 2018 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018 BASE Show details 18 Multilingual Neural Machine Translation with Task-Specific Attention ... Blackwood, Graeme; Ballesteros, Miguel; Ward, Todd. - : arXiv, 2018 Abstract: Multilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation. Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task. Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possible translation directions, compared to a model in which all parameters are shared. We observe improved translation quality even in the (extreme) low-resource zero-shot translation directions for which the model never saw explicitly paired parallel data. ... : COLING 2018 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1806.03280 https://dx.doi.org/10.48550/arxiv.1806.03280 BASE Hide details 19 Scheduled Multi-Task Learning: From Syntax to Translation ... Kiperwasser, Eliyahu; Ballesteros, Miguel. - : arXiv, 2018 BASE Show details 20 SemEval 2018 Task 2: Multilingual Emoji Prediction Barbieri, Francesco; Camacho-Collados, Jose; Ronzano, Francesco. - : The Association for Computational Linguistics, 2018 BASE Show details Page: 1 2 3

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