Automatic post-editing (APE), which aims to correct errors in the output of
machine translation systems in a post-processing step, is an important task in
natural language processing. While recent work has achieved considerable
performance gains by using neural networks, how to model the copying mechanism
for APE remains a challenge. In this work, we propose a new method for modeling
copying for APE. To better identify translation errors, our method learns the
representations of source sentences and system outputs in an interactive way.
These representations are used to explicitly indicate which words in the system
outputs should be copied, which is useful to help CopyNet (Gu et al., 2016)
better generate post-edited translations. Experiments on the datasets of the
WMT 2016-2017 APE shared tasks show that our approach outperforms all best
published results.

该研究提出了一种新的模拟自动后期编辑过程中的复制机制的方法，通过在源句子和系统输出之间交互学习来更好地识别翻译错误，并在 WMT 数据集上的实验中进行验证，表现优于以往最佳结果。

自动后编辑的复制学习

Learning to Copy for Automatic Post-Editing

In the current work, we present a description of the system submitted to WMT
2018 News Translation Shared task. The system was created to translate news
text from Finnish to English. The system used a Character Based Neural Machine
Translation model to accomplish the given task. The current paper documents the
preprocessing steps, the description of the submitted system and the results
produced using the same. Our system garnered a BLEU score of 12.9.

该论文描述了用于 WMT 2018 新闻翻译任务的系统，它使用了基于字符的神经机器翻译模型，进行从芬兰语到英语的新闻文本翻译，并获得了 12.9 的 BLEU 分数。

JUCBNMT 在 WMT2018 新闻翻译任务中的表现：基于字符的芬兰语到英语神经机器翻译

JUCBNMT at WMT2018 News Translation Task: Character Based Neural Machine  Translation of Finnish to English

Recent advances in statistical machine translation via the adoption of neural
sequence-to-sequence models empower the end-to-end system to achieve
state-of-the-art in many WMT benchmarks. The performance of such machine
translation (MT) system is usually evaluated by automatic metric BLEU when the
golden references are provided for validation. However, for model inference or
production deployment, the golden references are prohibitively available or
require expensive human annotation with bilingual expertise. In order to
address the issue of quality evaluation (QE) without reference, we propose a
general framework for automatic evaluation of translation output for most WMT
quality evaluation tasks. We first build a conditional target language model
with a novel bidirectional transformer, named neural bilingual expert model,
which is pre-trained on large parallel corpora for feature extraction. For QE
inference, the bilingual expert model can simultaneously produce the joint
latent representation between the source and the translation, and real-valued
measurements of possible erroneous tokens based on the prior knowledge learned
from parallel data. Subsequently, the features will further be fed into a
simple Bi-LSTM predictive model for quality evaluation. The experimental
results show that our approach achieves the state-of-the-art performance in the
quality estimation track of WMT 2017/2018.

提出了一种基于双向 Transformer 和双向 LSTM 预测模型的机器翻译自动评估框架，能够对 WMT 的翻译结果进行质量评估，并在 WMT 2017/2018 的质量评估任务中取得了最好的性能表现。