Thus, this research proposes a novel NMT-SMT hybrid framework that optimizes the baseline NMT comprising encoder–decoder with attention, by incrementally training it with the best translations of the ...

In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed significant growth. Incorporating the copy mechanism with traditional encoder-decoder ...

The model’s robustness to domain shift and the adaptability to new domains are two key challenges for handling low-resource DA in NMT. Previous works have usually focused on only one aspect, and a ...

Specifically, an NMT system first reads the source sentence using an encoder to build a "thought" vector, a sequence of numbers that represents the sentence meaning; a decoder, then, processes the ...

Google's NMT is an end-to-end learning approach for automated translation, with the potential to overcome many of the weakness of conventional phrase-based translation systems. It consists of a deep ...

In this project, we implement a challenge-set based approach to the evaluation of examples of three main NMT architectures: convolutional neural network-based systems (CNN), recurrent neural ...