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arXiv · 2410.11056

Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data

Zhongtao Liu·Parker Riley·Daniel Deutsch·Alison Lui·Mengmeng Niu·Apu Shah·Markus Freitag
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Abstract

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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Zhongtao Liu, Parker Riley, Daniel Deutsch, Alison Lui, Mengmeng Niu, Apu Shah, Markus Freitag. 2024-10-14. Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data. https://arxiv.org/abs/2410.11056

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