Prediction Of Words In Statistical Machine Translation Using A Multilayer Perceptron

We propose to estimate the probability that a target word appears in the translation of a given source sentence using a multilayer perceptron. At the expense of ignoring word order and repetition, our model does not assume word alignments and consider all source words jointly when evaluating the probability of a target word. We compared our model against IBM1 which does not consider word order either. Our model was comparable with IBM1 when predicting the target words that should appear in the translation of a source sentence. When our model was extended to include alignment information, it surpassed IBM1 on all the metrics we used.

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