Many recent discourse relation classification approaches have focused on cross-lingual data augmentation , training models to better represent the relational arguments by using various neural network models, including feed-forward network (Rutherford et al., 2017) , convolutional neural networks (Zhang et al., 2015) , recurrent neural network (Ji et al., 2016;<cite> Bai and Zhao, 2018)</cite> , character-based (Qin et al., 2016) or formulating relation classification as an adversarial task (Qin et al., 2017) . However, previously proposed neural models still crucially lack a representation of the typical relations between sentences: to solve the task properly, a model should ideally be able to form discourse expectations, i.e., to represent the typical causes, consequences, next events or contrasts to a given event described in one relational argument, and then assess the content of the second relational argument with respect to these expectations (see Example 1).