This paper proposes a masking mechanism that learns the importance of each input token and masks unnecessary tokens for relation extraction. As a feature of relation classification models, the shortest path between target entities in the dependency tree of an input sentence is often employed since it is known to well capture important information for relation classification. However, this heuristic rule is inapplicable to exceptional relation expressions such as relations that require tokens outside of the path (e.g., the possessive “s”). We handle such inflexibility by employing a novel masking mechanism that learns a masking rule of important tokens. We performed the training in an end-to-end manner by using the loss of the relation classification task without the need for additional annotations. The experimental results show that our proposed method shows better classification performance than the models with the shortest path heuristics. Furthermore, the learned masks highly correspond to the shortest paths, while capturing some important tokens outside the shortest paths such as possessive “s”.