Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.

翻译：多任务学习（MTL）因其能够利用训练任务间的共性与差异来提升相关任务性能，在自然语言处理（NLP）领域日益流行。然而，如何基于训练任务的关联性实现多任务学习仍未得到充分理解。本综述回顾了NLP中多任务学习方法的最新进展，旨在将其归纳为两种基于任务关联性的通用多任务训练方法：(i)联合训练与(ii)多步训练。我们呈现了各类NLP下游应用中的实例，总结了任务关系，并探讨了这一前景广阔课题的未来方向。