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Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments
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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“Popular evaluation methods for natural language generation such as BLEU-460, ROUGE-L61, and METEOR62 primarily compare n-grams between two documents and may not effectively capture semantic similarities.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [62]
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“BLEU computes n-gram overlap and is representative for the family of text overlap-based natural language generation metrics such as CIDEr,31 METEOR,30 and ROUGE.32”
From Evaluating progress in automatic chest X-ray radiology report generation (2023) · cites this paper as [30]
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Metadata
- Authors
- Lavie, A., & Agarwal, A.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Lavie, Alon
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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