# How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus

arXiv: https://arxiv.org/abs/2111.00191 · HTML: https://mlsanity.com/paper/2111.00191/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 30 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [TransQuest: Translation Quality Estimation with Cross-lingual Transformers](https://mlsanity.com/paper/2011.01536/) (2020)
- [A Comprehensive Survey of Grammar Error Correction](https://mlsanity.com/paper/2005.06600/) (2020)

## Built on by (most cited first, 3 of 3)

- [DMOps: Data Management Operation and Recipes](https://mlsanity.com/paper/2301.01228/) (2023)
- [A Self-Supervised Automatic Post-Editing Data Generation Tool](https://mlsanity.com/paper/2111.12284/) (2021)
- [Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction](https://mlsanity.com/paper/2306.14377/) (2023)

## Abstract (arXiv)

This paper proposes a tool for efficiently constructing high-quality parallel corpora with minimizing human labor and making this tool publicly available. Our proposed construction process is based on neural machine translation (NMT) to allow for it to not only coexist with human translation, but also improve its efficiency by combining data quality control with human translation in a data-centric approach.