Traducción automática basada en caracteres y redes neuronales
Out of vocabulary words are still an open problem in translation systems. The aim of this work is trying to solve the problem of a limited vocabulary using translation levels smaller than words (e.g: characters). We want to take advantage of characteristics like word compounding, morphological and s...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/89965 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/89965 |
| Access Level: | acceso abierto |
| Palabra clave: | Character-based Translation Models Neural Machine Translation Machine Translation Traducción Automática Traducción Automática basada en Redes Neuronales Modelos de Traducción basados en Caracteres LENGUAJES Y SISTEMAS INFORMATICOS Máster Universitario en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital-Màster Universitari en Intel·ligència Artificial, Reconeixement de Formes i Imatge Digital |
| Sumario: | Out of vocabulary words are still an open problem in translation systems. The aim of this work is trying to solve the problem of a limited vocabulary using translation levels smaller than words (e.g: characters). We want to take advantage of characteristics like word compounding, morphological and semantical properties to try to translate unknown words from sub-word units already known. This way the translation model is not only able to deal with unseen words but also it will be capable of generating new words not seen during the training phase. |
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