  Word segmentation plays a pivotal role in improving any Arabic NLP
application. Therefore, a lot of research has been spent in improving its
accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii)
domain/dialect dependent. We explore three language-independent alternatives to
morphological segmentation using: i) data-driven sub-word units, ii) characters
as a unit of learning, and iii) word embeddings learned using a character CNN
(Convolution Neural Network). On the tasks of Machine Translation and POS
tagging, we found these methods to achieve close to, and occasionally surpass
state-of-the-art performance. In our analysis, we show that a neural machine
translation system is sensitive to the ratio of source and target tokens, and a
ratio close to 1 or greater, gives optimal performance.
