Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. This study presents a comprehensive investigation of 11 approaches for acquiring translation data, including human-only, machineonly, and hybrid approaches. Our findings demonstrate that human-machine collaboration can match or even exceed the quality of human-only translations, while being more cost-efficient. Error analysis reveals the complementary strengths between human and machine contributions, highlighting the effectiveness of collaborative methods. Cost analysis further demonstrates the economic benefits of human-machine collaboration methods, with some approaches achieving top-tier quality at around 60% of the cost of traditional methods. We release a publicly available dataset containing nearly 18,000 segments of varying translation quality with corresponding human ratings to facilitate future research.
@article{arxiv.2410.11056,
title = {Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data},
author = {Zhongtao Liu and Parker Riley and Daniel Deutsch and Alison Lui and Mengmeng Niu and Apu Shah and Markus Freitag},
journal= {arXiv preprint arXiv:2410.11056},
year = {2024}
}