# Automatic Correction of Human Translations

arXiv: https://arxiv.org/abs/2206.08593 · HTML: https://mlsanity.com/paper/2206.08593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task](https://mlsanity.com/paper/1804.05940/) (2018)
- [Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data](https://mlsanity.com/paper/1903.00138/) (2019)
- [Adding Interpretable Attention to Neural Translation Models Improves Word Alignment](https://mlsanity.com/paper/1901.11359/) (2019)
- [Text Simplification by Tagging](https://mlsanity.com/paper/2103.05070/) (2021)
- [eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing](https://mlsanity.com/paper/1803.07274/) (2018)
- [Can Automatic Post-Editing Improve NMT?](https://mlsanity.com/paper/2009.14395/) (2020)
- [Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing](https://mlsanity.com/paper/1605.04800/) (2016)
- [A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning](https://mlsanity.com/paper/1906.06253/) (2019)
- [Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing](https://mlsanity.com/paper/1905.13068/) (2019)
- [Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks](https://mlsanity.com/paper/2003.06381/) (2020)
- [Detecting over/under-translation errors for determining adequacy in human translations](https://mlsanity.com/paper/2104.00267/) (2021)

## Built on by (most cited first, 1 of 1)

- [Neural Machine Translation Models Can Learn to be Few-shot Learners](https://mlsanity.com/paper/2309.08590/) (2023)

## Abstract (arXiv)

We introduce translation error correction (TEC), the task of automatically correcting human-generated translations. Imperfections in machine translations (MT) have long motivated systems for improving translations post-hoc with automatic post-editing. In contrast, little attention has been devoted to the problem of automatically correcting human translations, despite the intuition that humans make distinct errors that machines would be well-suited to assist with, from typos to inconsistencies in translation conventions. To investigate this, we build and release the Aced corpus with three TEC datasets. We show that human errors in TEC exhibit a more diverse range of errors and far fewer translation fluency errors than the MT errors in automatic post-editing datasets, suggesting the need for dedicated TEC models that are specialized to correct human errors. We show that pre-training instead on synthetic errors based on human errors improves TEC F-score by as much as 5.1 points. We conducted a human-in-the-loop user study with nine professional translation editors and found that the assistance of our TEC system led them to produce significantly higher quality revised translations.