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.

提出了一种基于循环多任务学习框架的神经比喻识别方法，通过迭代执行子任务并将前一个子任务的输出作为当前子任务的附加输入，更好地探索了子任务之间的相互依赖关系。该方法在中文比喻识别方面用 BERT 模型取得了极大的性能提升。

循环多任务学习与本地注意力的神经比拟识别