Abstract
Automatic question generation according to an answer within the given passage is useful for many
applications, such as question answering system,
dialogue system, etc. Current neural-based methods mostly take two steps which extract several
important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework
to generate questions about these sentences. These
approaches neglect the semantic relations between
the answer and the context of the whole passage
which is sometimes necessary for answering the
question. To address this problem, we propose the
Weak Supervision Enhanced Generative Network
(WeGen) which automatically discovers relevant
features of the passage given the answer span in a
weak supervised manner to improve the quality of
generated questions. More specifically, we devise a
discriminator, Relation Guider, to capture the relations between the whole passage and the associated
answer and then the Multi-Interaction mechanism
is deployed to transfer the knowledge dynamically
for our question generation system. Experiments
show the effectiveness of our method in both automatic evaluations and human evaluations