# Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing

arXiv: https://arxiv.org/abs/2009.09126 · HTML: https://mlsanity.com/paper/2009.09126/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2109.07141/) (2021)
- [QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task](https://mlsanity.com/paper/2112.14890/) (2021)
- [Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement](https://mlsanity.com/paper/2209.05695/) (2022)
- [Synslator: An Interactive Machine Translation Tool with Online Learning](https://mlsanity.com/paper/2310.05025/) (2023)

## Abstract (arXiv)

With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators needs to be manually closed by post-editing. In this paper, we propose an end-to-end deep learning framework of the quality estimation and automatic post-editing of the machine translation output. Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model. To imitate the behavior of human translators, we design three efficient delegation modules -- quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them. We examine this approach with the English--German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance. We also verify that the certified translators can significantly expedite their post-editing processing with our model in human evaluation.