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Simile recognition is to detect simile sentences and to extract similecomponents, i.e., tenors and vehicles. It involves two subtasks: {\it similesentence classification} and {\it simile component extraction}. Recent work hasshown that standard multitask learning is effective for Chinese similerecognition, but it is still uncertain whether the mutual effects between thesubtasks have been well captured by simple parameter sharing. We propose anovel cyclic multitask learning framework for neural simile recognition, whichstacks the subtasks and makes them into a loop by connecting the last to thefirst. It iteratively performs each subtask, taking the outputs of the previoussubtask as additional inputs to the current one, so that the interdependencebetween the subtasks can be better explored. Extensive experiments show thatour framework significantly outperforms the current state-of-the-art model andour carefully designed baselines, and the gains are still remarkable usingBERT.

Neural Simile Recognition with Cyclic Multitask Learning and Local Attention