@inproceedings{sajjad-etal-2017-challenging,
    title = "Challenging Language-Dependent Segmentation for {A}rabic: An Application to Machine Translation and Part-of-Speech Tagging",
    author = "Sajjad, Hassan  and
      Dalvi, Fahim  and
      Durrani, Nadir  and
      Abdelali, Ahmed  and
      Belinkov, Yonatan  and
      Vogel, Stephan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-2095",
    doi = "10.18653/v1/P17-2095",
    pages = "601--607",
    abstract = "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.",
}