A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages
By Nizar Habash, Nasser Zalmout, Dima Taji, Hieu D. Hoang, Maverick Alzate. 2017
18 citations recorded by the source corpus.
We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic. Arab-Acquis consists of over 12,000 sentences from the JRC-Acquis (Acquis Communautaire) corpus translated twice by professional translators, once from English and once from French, and totaling over 600,000 words. The corpus follows previous data splits in the literature for tuning, development, and testing. We describe the corpus and how it was created. We also present the first benchmarking results on translating to and from Arabic for 22 European languages.
Subfields: Artificial Intelligence, Natural Language Processing
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Citations in the corpus
- Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020, background): “panish dropped and ambiguous pronouns with the English pronouns. glish was most recently contested at WMT’132 (Bojar et al., 2013), which offered participants 14,980,513 sentence pairs from Europarl3 (Habash et al., 2017), Common Crawl (Smith et al., 2013), the United Nations corpus4 (Ziemski et al.,”