# Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality

arXiv: https://arxiv.org/abs/1910.06204 · HTML: https://mlsanity.com/paper/1910.06204/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 7 of 7)

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- [API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs](https://mlsanity.com/paper/2402.15491/) (2024)
- [Extrinsic Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2212.10297/) (2022)
- [Assessing Human Editing Effort on LLM-Generated Texts via Compression-Based Edit Distance](https://mlsanity.com/paper/2412.17321/) (2024)
- [The Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent Behavior](https://mlsanity.com/paper/2608.11386/) (2026)
- [Looking under the Wrong Lamppost: On the Limitations of Automated Translation Quality Estimation](https://mlsanity.com/paper/2608.03577/) (2026)
- [LLMs in the Real World: Evaluating "AI" in Emergency Contexts](https://mlsanity.com/paper/2607.00019/) (2026)

## Abstract (arXiv)

Devising metrics to assess translation quality has always been at the core of machine translation (MT) research. Traditional automatic reference-based metrics, such as BLEU, have shown correlations with human judgements of adequacy and fluency and have been paramount for the advancement of MT system development. Crowd-sourcing has popularised and enabled the scalability of metrics based on human judgements, such as subjective direct assessments (DA) of adequacy, that are believed to be more reliable than reference-based automatic metrics. Finally, task-based measurements, such as post-editing time, are expected to provide a more detailed evaluation of the usefulness of translations for a specific task. Therefore, while DA averages adequacy judgements to obtain an appraisal of (perceived) quality independently of the task, and reference-based automatic metrics try to objectively estimate quality also in a task-independent way, task-based metrics are measurements obtained either during or after performing a specific task. In this paper we argue that, although expensive, task-based measurements are the most reliable when estimating MT quality in a specific task; in our case, this task is post-editing. To that end, we report experiments on a dataset with newly-collected post-editing indicators and show their usefulness when estimating post-editing effort. Our results show that task-based metrics comparing machine-translated and post-edited versions are the best at tracking post-editing effort, as expected. These metrics are followed by DA, and then by metrics comparing the machine-translated version and independent references. We suggest that MT practitioners should be aware of these differences and acknowledge their implications when deciding how to evaluate MT for post-editing purposes.