Abstract
This paper describes the Microsoft and University of Edinburgh submission to the Automatic Post-editing shared task at WMT2018. Based on training data and systems from the WMT2017 shared task, we re-implement our own models from the last shared task and introduce improvements based on extensive parameter sharing. Next we experiment with our implementation of dual-source transformer models and data selection for the IT domain. Our submissions decisively wins the SMT post-editing sub-task establishing the new state-of-the-art and is a very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on the rather weak results in the NMT sub-task, we hypothesize that neural-on-neural APE might not be actually useful.
Abstract (translated)
本文介绍了Microsoft和爱丁堡大学提交的WMT2018自动编辑后共享任务。基于WMT2017共享任务中的训练数据和系统，我们从上一个共享任务重新实现我们自己的模型，并基于广泛的参数共享引入改进。接下来，我们将尝试实现双源变压器模型和IT域的数据选择。我们的提交决定性地赢得了SMT后编辑子任务，建立了新的最新技术，并且在NMT子任务中非常接近第二（或相等，16.46 vs 16.50 TER）。基于NMT子任务中相当弱的结果，我们假设神经神经APE可能实际上并不实用。
URL
https://arxiv.org/abs/1809.00188
