Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet

This paper focuses on comparing between using Support Vector Machine based ranking (SVMRank) and Bidirectional LongShort-Term-Memory (bi-LSTM) neuralnetwork based sequence labeling in building a state-of-the-art Arabic part-ofspeech tagging system. Using SVMRank leads to state-of-the-art results, but with a fair amount of feature engineering. Using bi-LSTM, particularly when combined with word embeddings, may lead to competitive POS-tagging results by automatically deducing latent linguistic features. However, we show that augmenting biLSTM sequence labeling with some of the features that we used for the SVMRankbased tagger yields to further improvements. We also show that gains realized using embeddings may not be additive with the gains achieved due to features. We are open-sourcing both the SVMRank and the bi-LSTM based systems for the research community.