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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; Wanner, Leo. - : arXiv, 2021 Abstract: The adaptation of pretrained language models to solve supervised tasks has become a baseline in NLP, and many recent works have focused on studying how linguistic information is encoded in the pretrained sentence representations. Among other information, it has been shown that entire syntax trees are implicitly embedded in the geometry of such models. As these models are often fine-tuned, it becomes increasingly important to understand how the encoded knowledge evolves along the fine-tuning. In this paper, we analyze the evolution of the embedded syntax trees along the fine-tuning process of BERT for six different tasks, covering all levels of the linguistic structure. Experimental results show that the encoded syntactic information is forgotten (PoS tagging), reinforced (dependency and constituency parsing) or preserved (semantics-related tasks) in different ways along the fine-tuning process depending on the task. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2101.11492 https://dx.doi.org/10.48550/arxiv.2101.11492 BASE Hide 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 BASE Show 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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