Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation
Year: 2011
Type: article
Abstract: 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).
DOI: 10.7916/d8697bg2
Authors: Salloum, Wael Sameer, Habash, Nizar Y.
Institution: Columbia University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1544567521