Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder

Du, Li, Ding, Xiao, Liu, Ting, Li, Zhongyang

arXiv.org Artificial Intelligence 

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.

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