  Simile recognition is to detect simile sentences and to extract simile
components, i.e., tenors and vehicles. It involves two subtasks: {\it simile
sentence classification} and {\it simile component extraction}. Recent work has
shown that standard multitask learning is effective for Chinese simile
recognition, but it is still uncertain whether the mutual effects between the
subtasks have been well captured by simple parameter sharing. We propose a
novel cyclic multitask learning framework for neural simile recognition, which
stacks the subtasks and makes them into a loop by connecting the last to the
first. It iteratively performs each subtask, taking the outputs of the previous
subtask as additional inputs to the current one, so that the interdependence
between the subtasks can be better explored. Extensive experiments show that
our framework significantly outperforms the current state-of-the-art model and
our carefully designed baselines, and the gains are still remarkable using
BERT.
