An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
This work addresses improving machine translation quality through post-editing, but it is incremental as it builds on existing neural methods for a specific task.
The paper tackled automatic post-editing of machine translation output by exploring neural sequence-to-sequence architectures, and found that dual-attention models incorporating all available data improved on the best WMT-2016 shared task system and other published results.
In this work, 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 $mt$ (raw MT output) and $src$ (source language input) in a single neural architecture, modeling $\{mt, src\} \rightarrow pe$ 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. We report results on data sets provided during the WMT-2016 shared task on automatic post-editing and can demonstrate that dual-attention models that incorporate all available data in the APE scenario in a single model improve on the best shared task system and on all other published results after the shared task. Dual-attention models that are combined with hard attention remain competitive despite applying fewer changes to the input.