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).
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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 |
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| Natural Language Processing Techniques | Computer Science |
| Topic Modeling | Computer Science |
| Text Readability and Simplification | Computer Science |
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