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Improving low-resource neural machine translation by semantic distance augmentation

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By Fuxue Li, Liaoning Province, de Gispert A
| PeerJ
Neural machine translation (NMT) grounded in the Transformer architecture (Vaswani et al., 2017) has attained state-of-the-art performance in translation tasks spanning a multitude of domains in recent years. In contrast to conventional statistical machine translation models, NMT employs an end-to-end neural network framework that autonomously acquires translation knowledge via an attention mechanism, markedly elevating the fluency and precision of translations.
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