%0 Conference Proceedings %T Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging %A Sajjad, Hassan %A Dalvi, Fahim %A Durrani, Nadir %A Abdelali, Ahmed %A Belinkov, Yonatan %A Vogel, Stephan %Y Barzilay, Regina %Y Kan, Min-Yen %S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) %D 2017 %8 July %I Association for Computational Linguistics %C Vancouver, Canada %F sajjad-etal-2017-challenging %X 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. %R 10.18653/v1/P17-2095 %U https://aclanthology.org/P17-2095 %U https://doi.org/10.18653/v1/P17-2095 %P 601-607