# A New Tool for Efficiently Generating Quality Estimation Datasets

arXiv: https://arxiv.org/abs/2111.00767 · HTML: https://mlsanity.com/paper/2111.00767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 1 Nov 2021.

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

- [Tailoring Domain Adaptation for Machine Translation Quality Estimation](https://mlsanity.com/paper/2304.08891/) (2023)

## Abstract (arXiv)

Building of data for quality estimation (QE) training is expensive and requires significant human labor. In this study, we focus on a data-centric approach while performing QE, and subsequently propose a fully automatic pseudo-QE dataset generation tool that generates QE datasets by receiving only monolingual or parallel corpus as the input. Consequently, the QE performance is enhanced either by data augmentation or by encouraging multiple language pairs to exploit the applicability of QE. Further, we intend to publicly release this user friendly QE dataset generation tool as we believe this tool provides a new, inexpensive method to the community for developing QE datasets.