Arabic Dialects present many challenges for machine translation, not least of which is the lack of data resources. We use crowdsourcing to cheaply and quickly build LevantineEnglish and Egyptian-English parallel corpora, consisting of 1.1M words and 380k words, respectively. The dialectal sentences are selected from a large corpus of Arabic web text, and translated using Amazon's Mechanical Turk. We use this data to build Dialectal Arabic MT systems, and find that small amounts of dialectal data have a dramatic impact on translation quality. When translating Egyptian and Levantine test sets, our Dialectal Arabic MT system performs 6.3 and 7.0 BLEU points higher than a Modern Standard Arabic MT system trained on a 150M-word Arabic-English parallel corpus.
REF used crowdsourcing to construct a 1.5 million word parallel corpus of dialect Arabic and English, training a statistical machine translation system that produced higher quality translations of dialect Arabic than a system a trained on 100 times more Modern Standard Arabic-English parallel data.
Machine Translation of Arabic Dialects
NAACL
Computer Science