Meteor Universal: Language Specific Translation Evaluation for Any Target Language
Michael Denkowski low, Alon Lavie
This paper describes Meteor Universal, released for the 2014 ACL Workshop on Statistical Machine Translation. Meteor Universal brings language specific evaluation to previously unsupported target languages by (1) automatically extracting linguistic resources (paraphrase tables and function word lists) from the bitext used to train MT systems and (2) using a universal parameter set learned from pooling human judgments of translation quality from several language directions. Meteor Universal is shown to significantly outperform baseline BLEU on two new languages, Russian (WMT13) and Hindi (WMT14).
Citation edge evidence
- Get To The Point: Summarization with Pointer-Generator Networks→Meteor Universal: Language Specific Translation Evaluation for Any Target LanguageLegacy edge — source detail unavailable
- BERTScore: Evaluating Text Generation with BERT→Meteor Universal: Language Specific Translation Evaluation for Any Target LanguageLegacy edge — source detail unavailable
- Meteor Universal: Language Specific Translation Evaluation for Any Target Language→MosesLegacy edge — source detail unavailable
- Meteor Universal: Language Specific Translation Evaluation for Any Target Language→Statistical phrase-based translationLegacy edge — source detail unavailable
- Meteor Universal: Language Specific Translation Evaluation for Any Target Language→Moses: Open Source Toolkit for Statistical Machine TranslationLegacy edge — source detail unavailable
- Moses→Statistical phrase-based translationLegacy edge — source detail unavailable
- BERTScore: Evaluating Text Generation with BERT→MosesLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Moses | 2007 | 4,889 |
| Statistical phrase-based translation | 2003 | 3,278 |
| Moses: Open Source Toolkit for Statistical Machine Translation | 2007 | 1,456 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Get To The Point: Summarization with Pointer-Generator Networks | 2017 | 3,935 |
| BERTScore: Evaluating Text Generation with BERT | 2019 | 2,065 |
Links
Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record
Topics
| Natural Language Processing Techniques | Computer Science |
| Topic Modeling | Computer Science |
| Text Readability and Simplification | Computer Science |
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