Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVMMohamed Eldesouki 1 , Younes Samih 2 ,Ahmed Abdelali 1 , Mohammed Attia 3 , Hamdy Mubarak 1 , Kareem Darwish 1 , and Laura Kallmeyer 21 Qatar Computing Research Institute, HBKU, Doha, Qatar2 Dept. of Computational Linguistics,University of Düsseldorf, Düsseldorf, Germany3 Google Inc., New York City, USA1 {mohamohamed,hmubarak,aabdelali,kdarwish}@hbku.edu.qa2 {samih,kallmeyer}@phil.hhu.de3 attia@google.comAbstractArabic word segmentation is essential fora variety of NLP applications such as ma-chine translation and information retrieval.Segmentation entails breaking words intotheir constituent stems, aff i xes and cli-tics. In this paper, we compare two ap-proaches for segmenting four major Ara-bic dialects using only several thousandtraining examples for each dialect. Thetwo approaches involve posing the prob-lem as a ranking problem, where an SVMranker picks the best segmentation, andas a sequence labeling problem, where abi-LSTM RNN coupled with CRF deter-mines where best to segment words. Weare able to achieve solid segmentation re-sults for all dialects using rather limitedtraining data. We also show that employ-ing Modern Standard Arabic data for do-main adaptation and assuming context in-dependence improve overall results.1 IntroductionArabic has both complex morphology and orthog-raphy, where stems are typically derived from aclosed set of roots to which aff i xes such as coor-dinating conjunctions, determiners, and pronounsare attached to form words. Segmenting Arabicwords into their constituent parts is important fora variety of natural language processing applica-tions. For example, segmentation has been shownto improve the effectiveness of information re-trieval (Darwish et al., 2014a) and machine trans-lation (Habash and Sadat, 2006). Most previouswork has mostly focused on segmenting ModernStandard Arabic (MSA) achieving segmentationaccuracies of nearly 99% (Abdelali et al., 2016;Pasha et al., 2014). MSA is the lingua franca ofthe Arab world, and it is typically used in writ-ten and formal communications. Dialectal Ara-bic (DA) segmentation on the other hand has re-ceived limited attention, with most of the workfocusing on the Egyptian dialect (Habash et al.,2013; Samih et al., 2017). Arabic dialects are typ-ically spoken and are used in informal communi-cations. The advent of the social media and theubiquity of smart phones has led to a greater needfor dialectal processing such as dialect identif i ca-tion (Eldesouki et al., 2016; Khurana et al., 2016),morphological analysis (Habash et al., 2013) andmachine translation (Sennrich et al., 2016; Sajjadet al., 2013). Yet, dialectal training corpora for avariety of NLP modules, including segmentation,continue to be limited and often nonexistent.In this work, we focus on the segmentation offour major Arabic dialects, namely Egyptian, Lev-antine, Gulf, and Maghrebi. We particularly focuson DA text from Twitter, a popular social mediaplatform, from which we can obtain large amountsof text in different dialects written by ordinary so-cial media users and exhibiting nonstandard or-thography. We employ two machine learning ap-proaches for building robust segmentation mod-ules using limited training data (350 tweets con-taining several thousand words per dialect). Inone approach, we pose the segmentation as a rank-ing problem where all possible segmentations ofa word are ranked using a Support Vector Ma-arXiv:1708.05891v1 [cs.CL] 19 Aug 2017
