This paper is about improving the quality of Arabic-English statistical machine translation (SMT) on 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 (OOV) words and low frequency words. Our approach extends an existing MSA analyzer with a small number of morphological clitics, and uses transfer rules to generate paraphrase lattices that are input to a state-of-the-art phrasebased SMT system. This approach improves BLEU scores on a blind test set by 0.56 absolute BLEU (or 1.5% relative). A manual error analysis of translated dialectal words shows that our system produces correct translations in 74% of the time for OOVs and 60% of the time for low frequency words.
Paraphrases from dialectal Arabic to standard Arabic have been used in REF to improve ArabicEnglish statistical machine translation.
Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation
Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties
Computer Science