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Supervised Attentions for Neural Machine Translation
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In this paper, we improve the attention or alignment accuracy of neural machine translation by utilizing the alignments of training sentence pairs. We simply compute the distance between the machine attentions and the "true" alignments, and minimize this cost in the training procedure. Our experiments on large-scale Chinese-to-English task show that our model improves both translation and alignment qualities significantly over the large-vocabulary neural machine translation system, and even beats a state-of-the-art traditional syntax-based system.
Forward citations
Cited by 2 Pith papers
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Regularized Context Gates on Transformer for Machine Translation
Adding context gates with PMI-based regularization to Transformer decoder layers yields an average +1.0 BLEU across four translation tasks.
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Capsule routing after multi-head attention gives small consistent BLEU improvements over Transformer in NMT, with EM routing slightly better than dynamic routing.
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