Consistent Human Evaluation of Machine Translation across Language Pairs

Daniel Licht, Cynthia Gao, Janice Lam, Francisco Guzman, Mona Diab, Philipp Koehn


Abstract
Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language pairs. We propose a new metric called XSTS that is more focused on semantic equivalence and a cross-lingual calibration method that enables more consistent assessment. We demonstrate the effectiveness of these novel contributions in large scale evaluation studies across up to 14 language pairs, with translation both into and out of English.
Anthology ID:
2022.amta-research.24
Volume:
Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
Month:
September
Year:
2022
Address:
Orlando, USA
Editors:
Kevin Duh, Francisco Guzmán
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
309–321
Language:
URL:
https://aclanthology.org/2022.amta-research.24
DOI:
Bibkey:
Cite (ACL):
Daniel Licht, Cynthia Gao, Janice Lam, Francisco Guzman, Mona Diab, and Philipp Koehn. 2022. Consistent Human Evaluation of Machine Translation across Language Pairs. In Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 309–321, Orlando, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Consistent Human Evaluation of Machine Translation across Language Pairs (Licht et al., AMTA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.amta-research.24.pdf
Data
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