  Generating inferential texts about an event in different perspectives
requires reasoning over different contexts that the event occurs. Existing
works usually ignore the context that is not explicitly provided, resulting in
a context-independent semantic representation that struggles to support the
generation. To address this, we propose an approach that automatically finds
evidence for an event from a large text corpus, and leverages the evidence to
guide the generation of inferential texts. Our approach works in an
encoder-decoder manner and is equipped with a Vector Quantised-Variational
Autoencoder, where the encoder outputs representations from a distribution over
discrete variables. Such discrete representations enable automatically
selecting relevant evidence, which not only facilitates evidence-aware
generation, but also provides a natural way to uncover rationales behind the
generation. Our approach provides state-of-the-art performance on both
Event2Mind and ATOMIC datasets. More importantly, we find that with discrete
representations, our model selectively uses evidence to generate different
inferential texts.
