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Automatic question generation according to an answer within the given passageis useful for many applications, such as question answering system, dialoguesystem, etc. Current neural-based methods mostly take two steps which extractseveral important sentences based on the candidate answer through manual rulesor supervised neural networks and then use an encoder-decoder framework togenerate questions about these sentences. These approaches neglect the semanticrelations between the answer and the context of the whole passage which issometimes necessary for answering the question. To address this problem, wepropose the Weak Supervision Enhanced Generative Network (WeGen) whichautomatically discovers relevant features of the passage given the answer spanin a weakly supervised manner to improve the quality of generated questions.More specifically, we devise a discriminator, Relation Guider, to capture therelations between the whole passage and the associated answer and then theMulti-Interaction mechanism is deployed to transfer the knowledge dynamicallyfor our question generation system. Experiments show the effectiveness of ourmethod in both automatic evaluations and human evaluations.

Weak Supervision Enhanced Generative Network for Question Generation