We demonstrate the feasibility of using unsupervised morphological segmentation for dialects of Arabic, which are poor in linguistics resources. Our experiments using a Qatari Arabic to English machine translation system show that unsupervised segmentation helps to improve the translation quality as compared to using no segmentation or to using ATB segmentation, which was especially designed for Modern Standard Arabic (MSA). We use MSA and other dialects to improve Qatari Arabic to English machine translation, and we show that a uniform segmentation scheme across them yields an improvement of 1.5 BLEU points over using no segmentation.

In the current article, we examine the long-run school selection patterns of children randomly assigned to the Chicago School Readiness Project, an early childhood educational (ECE) intervention that aimed to improve the quality of Head Start classrooms serving low-income communities. Analyses suggest that adolescents who participated in the program were more likely to opt out of their assigned neighborhood school and attend schools with better indicators of academic performance. Further analyses suggested that these selection patterns began in elementary school, although elementary school quality explained only a small portion of the effect on high school selection. Results suggest that intensive ECE interventions could have lasting effects on children’s patterns of selection into later educational environments.

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