@inproceedings{liu-etal-2024-beyond-human,
title = "Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data",
author = "Liu, Zhongtao and
Riley, Parker and
Deutsch, Daniel and
Lui, Alison and
Niu, Mengmeng and
Shah, Apurva and
Freitag, Markus",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Ninth Conference on Machine Translation",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wmt-1.110",
pages = "1095--1106",
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, machine-only, 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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<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, machine-only, 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.</abstract>
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%0 Conference Proceedings
%T Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data
%A Liu, Zhongtao
%A Riley, Parker
%A Deutsch, Daniel
%A Lui, Alison
%A Niu, Mengmeng
%A Shah, Apurva
%A Freitag, Markus
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F liu-etal-2024-beyond-human
%X 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, machine-only, 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.
%U https://aclanthology.org/2024.wmt-1.110
%P 1095-1106
Markdown (Informal)
[Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data](https://aclanthology.org/2024.wmt-1.110) (Liu et al., WMT 2024)
ACL
- Zhongtao Liu, Parker Riley, Daniel Deutsch, Alison Lui, Mengmeng Niu, Apurva Shah, and Markus Freitag. 2024. Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data. In Proceedings of the Ninth Conference on Machine Translation, pages 1095–1106, Miami, Florida, USA. Association for Computational Linguistics.