Arabic Dialect Identification Using a Parallel Multidialectal Corpus

Abstract

We present a study on sentence-level Arabic Dialect Identification using the newly developed Multidialectal Parallel Corpus of Arabic (MPCA) – the first experiments on such data. Using a set of surface features based on characters and words, we conduct three experiments with a linear Support Vector Machine classifier and a meta-classifier using stacked generalization – a method not previously applied for this task. We first conduct a 6-way multi-dialect classification task in the first experiment, achieving $74\,\%$ accuracy against a random baseline of $16.7\,\%$ and demonstrating that meta-classifiers can large performance increases over single classifiers. The second experiment investigates pairwise binary dialect classification within the corpus, yielding results as high as $94\,\%$, but also highlighting poorer results between closely related dialects such as Palestinian and Jordanian ($76\,\%$). Our final experiment conducts cross-corpus evaluation on the widely used Arabic Online Commentary (AOC) dataset and demonstrates that despite differing greatly in size and content, models trained with the MPCA generalize to the AOC, and vice versa. Using only 2, 000 sentences from the MPCA, we classify over 26 k sentences from the radically different AOC dataset with $74\,\%$ accuracy. We also use this data to classify a new dataset of MSA and Egyptian Arabic tweets with $97\,\%$ accuracy. We find that character n-g are a very informative feature for this task, in both within- and cross-corpus settings. Contrary to previous results, they outperform word n-grams in several experiments here. Several directions for future work are outlined.