Cited by 1,047Natural Language Processing TechniquesTopic ModelingTranslation Studies and Practices

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Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments

Lavie, Alon, Agarwal, Abhaya·2007·Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07

Abstract

Meteor is an automatic metric for Machine Translation evaluation which has been demonstrated to have high levels of correlation with human judgments of translation quality, significantly outperforming the more commonly used Bleu metric. It is one of several automatic metrics used in this year's shared task within the ACL WMT-07 workshop. This paper recaps the technical details underlying the metric and describes recent improvements in the metric. The latest release includes improved metric parameters and extends the metric to support evaluation of MT output in Spanish, French and German, in addition to English.

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Metadata

Authors
Lavie, A., & Agarwal, A.
Affiliations
Carnegie Mellon University (US)
Venue
Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07
Published
2007
Pages
228-231
OpenAlex
W2133512280
Cited by
1,047
References
10
Citations by year
2026: 50 · 2025: 104 · 2024: 91 · 2023: 80 · 2022: 78 · 2021: 124 · 2020: 98 · 2019: 84 · 2018: 43 · 2017: 24 · 2016: 29 · 2015: 32 · 2014: 23 · 2013: 23 · 2012: 30
FWCI
21.44
Type
conference paper
Language
en
Open access
closed
Locations
Conference on Machine Translation (WMT) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Social Sciences › Arts and Humanities › Language and Linguistics › Translation Studies and Practices
Keywords
meteors, machine translation evaluation, automatic evaluation metrics, translation quality, BLEU, human judgment correlation, multilingual evaluation, Spanish language, French language, German

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