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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 – 11 of 11 1 English machine reading comprehension: new approaches to answering multiple-choice questions Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021 In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021) BASE Show details 2 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 gaBERT -- an Irish Language Model ... Barry, James; Wagner, Joachim; Cassidy, Lauren; Cowap, Alan; Lynn, Teresa; Walsh, Abigail; Meachair, Mícheál J. Ó; Foster, Jennifer. - : arXiv, 2021 Abstract: The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many Natural Language Processing tasks. Over 120 monolingual BERT models covering over 50 languages have been released, as well as a multilingual model trained on 104 languages. We introduce, gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We release gaBERT and related code to the community. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2107.12930 https://arxiv.org/abs/2107.12930 BASE Hide details 6 Revisiting Tri-training of Dependency Parsers ... Wagner, Joachim; Foster, Jennifer. - : arXiv, 2021 BASE Show details 7 Improving Unsupervised Question Answering via Summarization-Informed Question Generation ... Lyu, Chenyang; Shang, Lifeng; Graham, Yvette. - : arXiv, 2021 BASE Show details 8 The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Barry, James; Foster, Jennifer. - : Underline Science Inc., 2021 BASE Show details 9 Revisiting Tri-training of Dependency Parsers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Foster, Jennifer; Wagner, Joachim. - : Underline Science Inc., 2021 BASE Show details 10 The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task Wagner, Joachim; Foster, Jennifer; Mohammadshahi, Alireza... In: http://infoscience.epfl.ch/record/289182 (2021) BASE Show details 11 English Machine Reading Comprehension Datasets: A Survey ; Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing Vogel, Carl; Foster, Jennifer; Dzendzik, Daria. - : Association for Computational Linguistics, 2021 BASE Show details

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