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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 – 6 of 6 1 Multi-Adversarial Learning for Cross-Lingual Word Embeddings ... NAACL 2021 2021; Henderson, James; Merlo, Paola. - : Underline Science Inc., 2021 BASE Show details 2 Multi-Adversarial Learning for Cross-Lingual Word Embeddings ... Wang, Haozhou; Henderson, James; Merlo, Paola. - : arXiv, 2020 BASE Show details 3 Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings Merlo, Paola; Henderson, James; Wang, Haozhou In: http://infoscience.epfl.ch/record/275419 (2020) BASE Show details 4 Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings ... Wang, Haozhou; Henderson, James; Merlo, Paola. - : arXiv, 2019 BASE Show details 5 CoNLL 2017 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details 6 CLCL (Geneva) DINN Parser : a Neural Network Dependency Parser Ten Years Later Moor, Christophe; Merlo, Paola; Henderson, James; Wang, Haozhou In: Proceedings of the CoNLL 2017 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies P. 228–236 (2017) Abstract: This paper describes the University of Geneva's submission to the CoNLL 2017 shared task Multilingual Parsing from Raw Text to Universal Dependencies (listed as the CLCL (Geneva) entry). Our submitted parsing system is the grandchild of the first transition-based neural network dependency parser, which was the University of Geneva's entry in the CoNLL 2007 multilingual dependency parsing shared task, with some improvements to speed and portability. These results provide a baseline for investigating how far we have come in the past ten years of work on neural network dependency parsing. Keyword: info:eu-repo/classification/ddc/025.063; info:eu-repo/classification/ddc/410 URL: https://archive-ouverte.unige.ch/unige:139253 BASE Hide details

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