# Assessing Post-editing Effort in the English-Hindi Direction

arXiv: https://arxiv.org/abs/2112.09841 · HTML: https://mlsanity.com/paper/2112.09841/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain](https://mlsanity.com/paper/1906.01685/) (2019)

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

- [DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages](https://mlsanity.com/paper/2205.12215/) (2022)

## Abstract (arXiv)

We present findings from a first in-depth post-editing effort estimation study in the English-Hindi direction along multiple effort indicators. We conduct a controlled experiment involving professional translators, who complete assigned tasks alternately, in a translation from scratch and a post-edit condition. We find that post-editing reduces translation time (by 63%), utilizes fewer keystrokes (by 59%), and decreases the number of pauses (by 63%) when compared to translating from scratch. We further verify the quality of translations thus produced via a human evaluation task in which we do not detect any discernible quality differences.