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
Venues:
EMNLP | IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2019.iwslt-1.23
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/2019.iwslt-1.23.pdf
Code
 carolscarton/iwslt2019
Data
IWSLT 2019