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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 – 7 of 7 1 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie... In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020) BASE Show details 2 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ... Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie. - : arXiv, 2020 BASE Show details 3 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ... Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie. - : Zenodo, 2020 BASE Show details 4 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ... Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie. - : Zenodo, 2020 BASE Show details 5 Linguistically inspired morphological inflection with a sequence to sequence model ... Metheniti, Eleni; Neumann, Guenter; van Genabith, Josef. - : arXiv, 2020 Abstract: Inflection is an essential part of every human language's morphology, yet little effort has been made to unify linguistic theory and computational methods in recent years. Methods of string manipulation are used to infer inflectional changes; our research question is whether a neural network would be capable of learning inflectional morphemes for inflection production in a similar way to a human in early stages of language acquisition. We are using an inflectional corpus (Metheniti and Neumann, 2020) and a single layer seq2seq model to test this hypothesis, in which the inflectional affixes are learned and predicted as a block and the word stem is modelled as a character sequence to account for infixation. Our character-morpheme-based model creates inflection by predicting the stem character-to-character and the inflectional affixes as character blocks. We conducted three experiments on creating an inflected form of a word given the lemma and a set of input and target features, comparing our architecture to ... : 13 pages, 6 figures ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2009.02073 https://dx.doi.org/10.48550/arxiv.2009.02073 BASE Hide details 6 Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ... Xu, Hongfei; van Genabith, Josef; Liu, Qiuhui. - : arXiv, 2020 BASE Show details 7 Language service provision in the 21st century: challenges, opportunities and educational perspectives for translation studies Bernardini, Silvia; Bouillon, Pierrette; Ciobanu, Dragos... In: ISBN: 9788869234934 ; Bologna Process beyond 2020: Fundamental values of the EHEA pp. 297-303 (2020) BASE Show details

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