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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 – 9 of 9 1 Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ... Ruiter, Dana; Klakow, Dietrich; van Genabith, Josef. - : arXiv, 2021 BASE Show details 2 Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ... Pylypenko, Daria; Amponsah-Kaakyire, Kwabena; Chowdhury, Koel Dutta. - : arXiv, 2021 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. - : arXiv, 2020 BASE Show details 4 Linguistically inspired morphological inflection with a sequence to sequence model ... Metheniti, Eleni; Neumann, Guenter; van Genabith, Josef. - : arXiv, 2020 BASE Show details 5 Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ... Xu, Hongfei; van Genabith, Josef; Liu, Qiuhui. - : arXiv, 2020 BASE Show details 6 INFODENS: An Open-source Framework for Learning Text Representations ... Taie, Ahmad; Rubino, Raphael; van Genabith, Josef. - : arXiv, 2018 BASE Show details 7 Massively Multilingual Neural Grapheme-to-Phoneme Conversion ... Peters, Ben; Dehdari, Jon; van Genabith, Josef. - : arXiv, 2017 BASE Show details 8 An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification ... España-Bonet, Cristina; Varga, Ádám Csaba; Barrón-Cedeño, Alberto; van Genabith, Josef. - : arXiv, 2017 Abstract: End-to-end neural machine translation has overtaken statistical machine translation in terms of translation quality for some language pairs, specially those with large amounts of parallel data. Besides this palpable improvement, neural networks provide several new properties. A single system can be trained to translate between many languages at almost no additional cost other than training time. Furthermore, internal representations learned by the network serve as a new semantic representation of words -or sentences- which, unlike standard word embeddings, are learned in an essentially bilingual or even multilingual context. In view of these properties, the contribution of the present work is two-fold. First, we systematically study the NMT context vectors, i.e. output of the encoder, and their power as an interlingua representation of a sentence. We assess their quality and effectiveness by measuring similarities across translations, as well as semantically related and semantically unrelated sentence pairs. ... : 11 pages, 4 figures ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1704.05415 https://dx.doi.org/10.48550/arxiv.1704.05415 BASE Hide details 9 Predicting the Law Area and Decisions of French Supreme Court Cases ... Sulea, Octavia-Maria; Zampieri, Marcos; Vela, Mihaela. - : arXiv, 2017 BASE Show details

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