Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality
Scarton Scarton, Mikel L. Forcada, Miquel Esplà-Gomis, Lucia Specia
Abstract
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 judgments, 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 de- tailed 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 decid- ing how to evaluate MT for post-editing purposes.- Anthology ID:
- 2019.iwslt-1.23
- Volume:
- Proceedings of the 16th International Conference on Spoken Language Translation
- Month:
- November 2-3
- Year:
- 2019
- Address:
- Hong Kong
- Editors:
- Jan Niehues, Rolando Cattoni, Sebastian Stüker, Matteo Negri, Marco Turchi, Thanh-Le Ha, Elizabeth Salesky, Ramon Sanabria, Loic Barrault, Lucia Specia, Marcello Federico
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2019.iwslt-1.23
- DOI:
- Cite (ACL):
- Scarton Scarton, Mikel L. Forcada, Miquel Esplà-Gomis, and Lucia Specia. 2019. Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality. In Proceedings of the 16th International Conference on Spoken Language Translation, Hong Kong. Association for Computational Linguistics.
- Cite (Informal):
- Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality (Scarton et al., IWSLT 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2019.iwslt-1.23.pdf
- Code
- carolscarton/iwslt2019
- Data
- IWSLT 2019