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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 – 5 of 5 1 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 2 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 3 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 4 Encoders Help You Disambiguate Word Senses in Neural Machine Translation ... Tang, Gongbo; Sennrich, Rico; Nivre, Joakim. - : Association for Computational Linguistics, 2019 BASE Show details 5 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

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