How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus
This addresses the challenge of efficient corpus construction for translation tasks, but it appears incremental as it builds on existing NMT methods.
The paper tackles the problem of building high-quality parallel corpora by proposing a tool that minimizes human labor and makes it publicly available, using neural machine translation to coexist with and improve human translation efficiency through a data-centric approach.
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.