Learning to Transfer via Modelling Multi-level Task Dependency

Multi-task learning has been successful in modeling multiple related tasks with large, carefully curated labeled datasets. By leveraging the relationships among different tasks, multi-task learning framework can improve the performance significantly. However, most of the existing works are under the assumption that the predefined tasks are related to each other. Thus, their applications on real-world are limited, because rare real-world problems are closely related. Besides, the understanding of relationships among tasks has been ignored by most of the current methods. Along this line, we propose a novel multi-task learning framework - Learning To Transfer Via Modelling Multi-level Task Dependency, which constructed attention based dependency relationships among different tasks. At the same time, the dependency relationship can be used to guide what knowledge should be transferred, thus the performance of our model also be improved. To show the effectiveness of our model and the importance of considering multi-level dependency relationship, we conduct experiments on several public datasets, on which we obtain significant improvements over current methods.