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Arabic word segmentation is essential for a variety of NLP applications suchas machine translation and information retrieval. Segmentation entails breakingwords into their constituent stems, affixes and clitics. In this paper, wecompare two approaches for segmenting four major Arabic dialects using onlyseveral thousand training examples for each dialect. The two approaches involveposing the problem as a ranking problem, where an SVM ranker picks the bestsegmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupledwith CRF determines where best to segment words. We are able to achieve solidsegmentation results for all dialects using rather limited training data. Wealso show that employing Modern Standard Arabic data for domain adaptation andassuming context independence improve overall results.

Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM