Machine translation is the task of translating a sentence in a source language to a different target language
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We present Neural Phrase-to-Phrase Machine Translation (\nppmt), a phrase-based translation model that uses a novel phrase-attention mechanism to discover relevant input (source) segments to generate output (target) phrases.
In this paper, we propose the use of in-training matrix factorization to reduce the model size for neural machine translation.
We investigate the sensitivity of such models to the value of k that is used during training and when deploying the model, and the effect of updating the hidden states in transformer models as new source tokens are read.
Experimental results show that the proposed method has significant improvement over state of the art methods, and it enables knowledge transfer and prevents catastrophic forgetting, resulting in more than 85% accuracy up to 100 stages, compared with less 50% accuracy for baselines.
Self-supervised neural machine translation (SS-NMT) learns how to extract/select suitable training data from comparable (rather than parallel) corpora and how to translate, in a way that the two tasks support each other in a virtuous circle.