This work describes the AMU-UEdin submission to the WMT 2017 shared task on Automatic Post-Editing. We explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputs mt and src in a single neural architecture, modeling {mt,src} \u2192 pe directly. Apart from that, we investigate the influence of hard-attention models which seem to be well-suited for monolingual tasks, as well as combinations of both ideas.<\/p>\n","protected":false},"excerpt":{"rendered":"
This work describes the AMU-UEdin submission to the WMT 2017 shared task on Automatic Post-Editing. We explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputs mt and src in a single neural architecture, modeling {mt,src} \u2192 pe directly. 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