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Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation

Salloum, Wael Sameer; Habash, Nizar Y.

This paper is interested in improving the quality of Arabic-English statistical machine translation (SMT) on highly dialectal Arabic text using morphological knowledge. We present a light-weight rule-based approach to producing Modern Standard Arabic (MSA) paraphrases of dialectal Arabic out-of-vocabulary words and low frequency words. Our approach extends an existing MSA analyzer with a small number of morphological clitics and transfer rules. The generated paraphrase lattices are input to a state-of-the-art phrase-based SMT system resulting in improved BLEU scores on a blind test set by 0.56 absolute BLEU (or 1.5% relative).



More About This Work

Academic Units
Center for Computational Learning Systems
Center for Computational Learning Systems, Columbia University
CCLS Technical Report, CCLS-11-01
Published Here
May 6, 2011
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