Translate your Own: a Post-Editing Experiment in the NLP domain
Résumé
The improvements in neural machine translation make translation and post- editing pipelines ever more effective for a wider range of applications. In this paper, we evaluate the effectiveness of such a pipeline for the translation of scientific documents (limited here to article abstracts). Using a dedicated interface, we collect, then analyse the post-edits of approximately 350 abstracts (English→French) in the Natural Lan- guage Processing domain for two groups of post-editors: domain experts (academics encouraged to post-edit their own articles) on the one hand and trained translators on the other. Our results confirm that such pipelines can be effective, at least for high-resource language pairs. They also highlight the difference in the post-editing strategy of the two subgroups. Finally, they suggest that working on term translation is the most pressing issue to improve fully automatic translations, but that in a post-editing setup, other error types can be equally annoying for post-editors.
Domaines
Informatique et langage [cs.CL]| Origine | Fichiers produits par l'(les) auteur(s) |
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