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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 – 13 of 13 1 Syntactic Nuclei in Dependency Parsing -- A Multilingual Exploration ... Basirat, Ali; Nivre, Joakim. - : arXiv, 2021 BASE Show details 2 Attention Can Reflect Syntactic Structure (If You Let It) ... Ravishankar, Vinit; Kulmizev, Artur; Abdou, Mostafa. - : arXiv, 2021 BASE Show details 3 Schrödinger's Tree -- On Syntax and Neural Language Models ... Kulmizev, Artur; Nivre, Joakim. - : arXiv, 2021 BASE Show details 4 Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding ... Hershcovich, Daniel; de Lhoneux, Miryam; Kulmizev, Artur. - : arXiv, 2020 BASE Show details 5 Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English ... Tang, Gongbo; Sennrich, Rico; Nivre, Joakim. - : arXiv, 2020 BASE Show details 6 Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ... Nivre, Joakim; de Marneffe, Marie-Catherine; Ginter, Filip. - : arXiv, 2020 BASE Show details 7 Do Neural Language Models Show Preferences for Syntactic Formalisms? ... Kulmizev, Artur; Ravishankar, Vinit; Abdou, Mostafa; Nivre, Joakim. - : arXiv, 2020 Abstract: Recent work on the interpretability of deep neural language models has concluded that many properties of natural language syntax are encoded in their representational spaces. However, such studies often suffer from limited scope by focusing on a single language and a single linguistic formalism. In this study, we aim to investigate the extent to which the semblance of syntactic structure captured by language models adheres to a surface-syntactic or deep syntactic style of analysis, and whether the patterns are consistent across different languages. We apply a probe for extracting directed dependency trees to BERT and ELMo models trained on 13 different languages, probing for two different syntactic annotation styles: Universal Dependencies (UD), prioritizing deep syntactic relations, and Surface-Syntactic Universal Dependencies (SUD), focusing on surface structure. We find that both models exhibit a preference for UD over SUD - with interesting variations across languages and layers - and that the strength ... : ACL 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2004.14096 https://arxiv.org/abs/2004.14096 BASE Hide details 8 Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited ... Kulmizev, Artur; de Lhoneux, Miryam; Gontrum, Johannes. - : arXiv, 2019 BASE Show details 9 Encoders Help You Disambiguate Word Senses in Neural Machine Translation ... Tang, Gongbo; Sennrich, Rico; Nivre, Joakim. - : arXiv, 2019 BASE Show details 10 82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models ... Smith, Aaron; Bohnet, Bernd; de Lhoneux, Miryam. - : arXiv, 2018 BASE Show details 11 An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation ... Tang, Gongbo; Sennrich, Rico; Nivre, Joakim. - : arXiv, 2018 BASE Show details 12 Dependency Parsing of Turkish ... Eryigit, Gulsen; Nivre, Joakim; Oflazer, Kemal. - : Carnegie Mellon University, 2008 BASE Show details 13 Dependency Parsing of Turkish ... Eryigit, Gulsen; Nivre, Joakim; Oflazer, Kemal. - : Carnegie Mellon University, 2008 BASE Show details

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