الفهرس 
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Abstract
For many real-world machine learning applications, labeled data is costly because the data labeling process is laborious and time consuming. As a consequence, only limited labeled data is available for model training, leading to the so-called labeled data deficiency problem. In the machine learning research community, several directions have been pursued to address this problem. Among these efforts, a promising direction is multi-task learning which is a learning paradigm that seeks to boost the generalization performance of a model on a learning task with the help of some other related tasks. This learning paradigm has been inspired by human learning activities in that people often apply the knowledge gained from previous learning tasks to help learn a new task more efficiently and effectively. Multi-task learning is a method that was proposed to solve the problem of separate tasks.