A Bidirectional Recurrent Neural Language Model for Machine Translation

• Autores: Àlvar Peris Blanes , Francisco Casacuberta Nolla
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 55, 2015, págs. 109-116
• Idioma: inglés
• Títulos paralelos:
  • Un modelo de lenguaje neuronal recurrente bidireccional para la traducci´on autom´atica
• Enlaces
  • Texto completo
• Resumen
  • español

    Se presenta un modelo de lenguaje basado en representaciones continuas de las palabras, el cual se ha aplicado a una tarea de traducción automática estadística. Este modelo está implementado por una red neuronal recurrente bidireccional, la cual es capaz de tener en cuenta el contexto pasado y futuro de una palabra para realizar predicciones. Debido su alto coste temporal de entrenamiento, para obtener datos de entrenamiento relevantes se emplea un algoritmo de selección de oraciones, el cual busca capturar información útil para traducir un determinado conjunto de test. Los resultados obtenidos muestran que el modelo neuronal entrenado con los datos seleccionados es capaz de mejorar los resultados obtenidos por un modelo de lenguaje de n-gramas.

  • English

    A language model based in continuous representations of words is presented, which has been applied to a statistical machine translation task. This model is implemented by means of a bidirectional recurrent neural network, which is able to take into account both the past and the future context of a word in order to perform predictions. Due to its high temporal cost at training time, for obtaining relevant training data an instance selection algorithm is used, which aims to capture useful information for translating a test set. Obtained results show that the neural model trained with the selected data outperforms the results obtained by an n-gram language model.

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