An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing

Title: An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing
Authors: Marcin Junczys-Dowmunt, Roman Grundkiewicz
Published: 13th June 2017 (Tuesday) @ 15:55:02
Link: http://arxiv.org/abs/1706.04138v2

Abstract

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 $\{mt,src\}\to pe$ 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.

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