How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus

TL;DR

This paper introduces a tool that leverages neural machine translation to efficiently build high-quality parallel corpora, reducing human effort and enhancing translation data quality.

Contribution

The paper presents a novel data-centric tool that combines NMT and human translation to improve efficiency in creating parallel corpora.

Findings

Reduces human labor in corpus construction

Improves data quality through combined NMT and human input

Tool is publicly available for broader use

Abstract

This paper proposes a tool for efficiently constructing high-quality parallel corpora with minimizing human labor and making this tool publicly available. Our proposed construction process is based on neural machine translation (NMT) to allow for it to not only coexist with human translation, but also improve its efficiency by combining data quality control with human translation in a data-centric approach.