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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 – 4 of 4 1 Domain adaptation for statistical machine translation and neural machine translation Zhang, Jian. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017 In: Zhang, Jian orcid:0000-0001-5659-5865 (2017) Domain adaptation for statistical machine translation and neural machine translation. PhD thesis, Dublin City University. (2017) BASE Show details 2 Machine translation of morphologically rich languages using deep neural networks Passban, Peyman. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017 In: Passban, Peyman (2017) Machine translation of morphologically rich languages using deep neural networks. PhD thesis, Dublin City University. (2017) BASE Show details 3 Dependency graph-based statistical machine translation Li, Liangyou. - : Dublin City University. School of Computing, 2016 In: Li, Liangyou orcid:0000-0002-0279-003X (2016) Dependency graph-based statistical machine translation. PhD thesis, Dublin City University. (2016) BASE Show details 4 Parallel FDA5 for fast deployment of accurate statistical machine translation systems Liu, Qun; Bicici, Ergun; Way, Andy In: Bicici, Ergun, Liu, Qun and Way, Andy orcid:0000-0001-5736-5930 (2014) Parallel FDA5 for fast deployment of accurate statistical machine translation systems. In: ACL 2014, Ninth Workshop on Statistical MachineTranslation, 26-27 June, 2014, Baltimore, USA. (2014) Abstract: We use parallel FDA5, an efficiently parameterized and optimized parallel implementation of feature decay algorithms for fast deployment of accurate statistical machine translation systems, taking only about half a day for each translation direction. We build Parallel FDA5 Moses SMT systems for all language pairs in the WMT14 translation task and obtain SMT performance close to the top Moses systems with an average of $3.49$ BLEU points difference using significantly less resources for training and development. Keyword: Computational linguistics; Information retrieval; Machine translating URL: http://doras.dcu.ie/19989/ BASE Hide details

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