人机协作可高效获取高质量翻译数据，成本仅需人工的60%
Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data
- 采用11种方法对比人机协作与纯人工翻译
- 混合方法质量媲美甚至超越纯人工，成本降低约40%
- 适合需要大规模高质量翻译数据的研究者
高质量翻译数据对机器翻译系统的发展与评估至关重要。传统纯人工方法成本高、效率低。本研究系统比较了11种翻译数据收集方法，涵盖纯人工、纯机器及混合方式。结果表明，人机协作在保持或超过人工翻译质量的同时，显著降低成本。错误分析揭示了人类与机器在翻译中的互补优势，验证了协作的有效性。成本分析显示，部分方法可实现顶级质量，成本仅为传统方法的约60%。研究公开发布包含近18,000个翻译片段的语料库，每个片段附有对应的人工评分，以支持未来研究。
原文摘要 · Abstract (English)
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.
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