To facilitate this, Rashkin et al. (2018) build the Event2Mind dataset and Sap et al. (2018) present the Atomic dataset, mainly focus on nine If-Then reasoning types to describe causes, effects, intents and participant characteristic about events. Together with these datasets, a simple RNN-based encoder-decoder framework is proposed to conduct the If-Then reasoning. However, there still remains two challenging problems. First, as illustrated in Figure 1, given an event "PersonX finds a new job", the plausible feeling of PersonX about that event could be multiple (such as "needy/stressed out" and "relieved/joyful"). Previous work showed that for the one-to-many problem, conventional RNN-based encoder-decoder models tend to generate generic responses, rather than meaningful and specific answers (Li et al., 2016; Serban et al., 2016). Second, as a commonsense reasoning problem, rich background knowledge is necessary for generating reasonable inferences. For example, as shown in Figure 1, the feeling of PersonX upon the event "PersonX finds a new job" could be multiple. However, after given a context " PersonX was fired", the plausible inferences would be narrowed down to " needy" or " stressed out ". To better solve these problems, we propose a context-aware variational autoencoder (CWV AE) together with a two-stage training procedure.