Automatic Post-Editing for Vietnamese

Thanh Vu, Dat Quoc Nguyen, Dai Quoc Nguyen

Abstract: 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 the first attempt 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. To facilitate future research and applications, we publicly release our dataset and baseline checkpoints at: \url{http://url-link}.