Character-based neural machine translation
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/105448
Tipus de documentComunicació de congrés
Data publicació2016
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Neural Machine Translation (MT) has reached state-of-the-art results. However, one of the main challenges that neural MT still faces is dealing with very large vocabularies and morphologically rich languages. In this paper, we propose a neural MT system using character-based embeddings in combination with convolutional and highway layers to replace the standard lookup-based word representations. The resulting unlimited-vocabulary and affixaware source word embeddings are tested in a state-of-the-art neural MT based on an attention-based bidirectional recurrent neural network. The proposed MT scheme provides improved results even when the source language is not morphologically rich. Improvements up to 3 BLEU points are obtained in the German-English WMT task.
CitacióRuiz, M., Fonollosa, J. A. R. Character-based neural machine translation. A: Annual Meeting of the Association for Computational Linguistics. "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)". 2016, p. 357-361.
ISBN978-1-945626-02-9
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
P16-2058.pdf | 166,0Kb | Visualitza/Obre |