Automatic Post-Editing for Vietnamese
This work addresses the need for improved translation quality in Vietnamese, though it is incremental as it applies existing methods to a new language-specific dataset.
The authors tackled the problem of automatic post-editing for Vietnamese by constructing the first large-scale dataset of 5 million sentence pairs and applying neural machine translation models, showing effectiveness in reducing errors through experimental evaluations.
Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.