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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. - : arXiv, 2020 BASE Show 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 Abstract: Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of disambiguation. In this paper, we explore the ability of NMT encoders and decoders to disambiguate word senses by evaluating hidden states and investigating the distributions of self-attention. We train a classifier to predict whether a translation is correct given the representation of an ambiguous noun. We find that encoder hidden states outperform word embeddings significantly which indicates that encoders adequately encode relevant information for disambiguation into hidden states. Decoders could provide further relevant information for disambiguation. Moreover, the attention weights and attention entropy show that self-attention can detect ambiguous nouns and distribute more attention to the context. Note that this is a revised version. The content related to decoder hidden states has been updated. ... : Update with corrections. Here is the link to the erratum: https://www.aclweb.org/anthology/D19-1149e1.pdf ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1908.11771 https://arxiv.org/abs/1908.11771 BASE Hide 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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