01 Apr21 Issue #124 – Towards Enhancing Faithfulness for Neural MT Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction While Neural Machine Translation is generally fluent, it occasionally can be deceptively so, either omitting or adding fragments. In today’s post we examine a method proposed to address this shortcoming and make the model more faithful to the source; Weng et al. (2020) propose a faithfulness-enhanced NMT model, called FENMT. The Problem They surmise that there are potentially 3 […]