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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; Wanner, Leo. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.118/ Abstract: Transformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. However, while pretraining methods are very convenient, they are expensive in terms of time and resources. This calls for a study of the impact of pretraining data size on the knowledge of the models. We explore this impact on the syntactic capabilities of RoBERTa, using models trained on incremental sizes of raw text data. First, we use syntactic structural probes to determine whether models pretrained on more data encode a higher amount of syntactic information. Second, we perform a targeted syntactic evaluation to analyze the impact of pretraining data size on the syntactic generalization performance of the models. Third, we compare the performance of the different models on three downstream applications: part-of-speech tagging, dependency parsing and paraphrase identification. We complement our study with an analysis of ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Natural Language Understanding URL: https://dx.doi.org/10.48448/v5rn-cz20 https://underline.io/lecture/37702-how-much-pretraining-data-do-language-models-need-to-learn-syntaxquestion BASE Hide 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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