Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data
Zhongtao Liu, Parker Riley, Daniel Deutsch, Alison Lui, Mengmeng Niu, Apurva Shah, Markus Freitag · 2024
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 1 containing nearly 18,000 segments of varying translation quality with corresponding human ratings to facilitate future research.