International Workshop on Spoken Language Translation (IWSLT) 2010

Paris, France
December 2-3, 2010

N-gram-based Machine Translation Enhanced with Neural Networks for the French-English BTEC-IWSLT'10 Task

Francisco Zamora-Martínez (1), María José Castro-Bleda (2), Holger Schwenk (3)

(1) Departamento de Ciencias Físicas, Matemáticas y de la Computación, Universidad CEU-Cardenal Herrera, Alfara del Patriarca (Valencia), Spain
(2) Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Spain
(3) Laboratoire d'Informatique d'Université du Maine Université du Mans, France

Neural Network Language Models (NNLMs) have been applied to Statistical Machine Translation (SMT) outperforming the translation quality. N-best list rescoring is the most popular approach to deal with the computational problems that appear when using huge NNLMs. But the question of “how much improvement could be achieved in a coupled system” remains unanswered. This open question motivated some previous work of us in order to speed the evaluation of NNLMs. Now, this work integrates the NNLM evaluation in the core of the SMT decoder. NNLMs are used in combination with statistical standard N-gram language models under the maximum entropy framework in an N-gram-based SMT system. A reordering decoder builds a reordering graph coupled during a Viterbi decoding.
   This N-gram-based SMT system enhanced with NNLMs for the French-English BTEC task of the IWSLT'10 evaluation campaign is described in detail. An improvement between 1.8 and 2.4 BLEU points was obtained from the baseline system to the official primary system. This system has been positioned as second in the automatic evaluation of the IWSLT'10 official results.

Full Paper

Bibliographic reference.  Zamora-Martínez, Francisco / Castro-Bleda, María José / Schwenk, Holger (2010): "N-gram-based machine translation enhanced with neural networks for the French-English BTEC-IWSLT'10 task", In IWSLT-2010, 45-52.