# Automatic Post-Editing for Vietnamese

arXiv: https://arxiv.org/abs/2104.12128 · HTML: https://mlsanity.com/paper/2104.12128/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [VnCoreNLP: A Vietnamese Natural Language Processing Toolkit](https://mlsanity.com/paper/1801.01331/) (2018)
- [A Fast and Accurate Vietnamese Word Segmenter](https://mlsanity.com/paper/1709.06307/) (2017)
- [A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning](https://mlsanity.com/paper/1906.06253/) (2019)
- [Sequence to Sequence Learning for Event Prediction](https://mlsanity.com/paper/1709.06033/) (2017)

## Abstract (arXiv)

Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.