This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1.2: Country-level DA identification. We compare the performance of a simple word-level LSTM using pretrained embeddings with one enhanced using feature embeddings for engineered linguistic features. Our results show that the addition of explicit features to the LSTM is detrimental to performance. We attribute this performance loss to the bivalency of some linguistic items in some text, ubiquity of topics, and participant mobility.

Arabic Broken Plurals show an interesting phenomenon in Arabic morphology as they are formed by shifting the consonants of the syllables into different syllable patterns, and subsequently, the pattern of the word changes. The present paper, therefore, attempts to look at Arabic broken plurals from the perspective of neural networks by implementing an OpenNMT experiment to better understand and interpret the behavior of these plurals, especially when it comes to L2 acquisition. The results show that the model is successful in predicting the Arabic template. However, it fails to predict certain consonants such as the emphatics and the gutturals. This reinforces the fact that these consonants or sounds are the most difficult for L2 learners to acquire.