Sharing information between multiple tasks enables algorithms to achieve good
generalization performance even from small amounts of training data. However,
in a realistic scenario of multi-task learning not all tasks are equally
related to each other, hence it could be advantageous to transfer information
only between the most related tasks. In this work we propose an approach that
processes multiple tasks in a sequence with sharing between subsequent tasks
instead of solving all tasks jointly. Subsequently, we address the question of
curriculum learning of tasks, i.e. finding the best order of tasks to be
learned. Our approach is based on a generalization bound criterion for choosing
the task order that optimizes the average expected classification performance
over all tasks. Our experimental results show that learning multiple related
tasks sequentially can be more effective than learning them jointly, the order
in which tasks are being solved affects the overall performance, and that our
model is able to automatically discover the favourable order of tasks.

本文提出了一种处理多个任务的顺序学习方法，通过仅在相关性较高的任务之间共享信息，其基于广义绑定准则的任务顺序决策能够优化任务分类绩效并自动发现任务最佳顺序。实验结果表明，顺序学习比同时学习更加有效，任务完成顺序重要并具有指引意义。