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Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputs
The present study aims to compare three systems: a generic statistical machine translation (SMT), a generic neural machine translation (NMT) and a tailored-NMT system focusing on the English to Greek language pair.
NoDeeLe: A Novel Deep Learning Schema for Evaluating Neural Machine Translation Systems
Due to the wide-spread development of Machine Translation (MT) systems –especially Neural Machine Translation (NMT) systems– MT evaluation, both automatic and human, has become more and more important as it helps us establish how MT systems perform.
Comparing a Hand-crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation
One using string based hand-crafted features (Exp1), the second using automatically trained embeddings from the reference and the two MT outputs (one from a statistical machine translation (SMT) model and the other from a neural ma-chine translation (NMT) model), which are learned using NN (Exp2), and the third experiment (Exp3) that combines information from the other two experiments.
