  Transformer-based models, even though achieving super-human performance on
several downstream tasks, are often regarded as a black box and used as a
whole. It is still unclear what mechanisms they have learned, especially their
core module: multi-head attention. Inspired by functional specialization in the
human brain, which helps to efficiently handle multiple tasks, this work
attempts to figure out whether the multi-head attention module will evolve
similar function separation under multi-tasking training. If it is, can this
mechanism further improve the model performance? To investigate these
questions, we introduce an interpreting method to quantify the degree of
functional specialization in multi-head attention. We further propose a simple
multi-task training method to increase functional specialization and mitigate
negative information transfer in multi-task learning. Experimental results on
seven pre-trained transformer models have demonstrated that multi-head
attention does evolve functional specialization phenomenon after multi-task
training which is affected by the similarity of tasks. Moreover, the multi-task
training strategy based on functional specialization boosts performance in both
multi-task learning and transfer learning without adding any parameters.
