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.
展开▼
