  We tackle the task of question generation over knowledge bases. Conventional
methods for this task neglect two crucial research issues: 1) the given
predicate needs to be expressed; 2) the answer to the generated question needs
to be definitive. In this paper, we strive toward the above two issues via
incorporating diversified contexts and answer-aware loss. Specifically, we
propose a neural encoder-decoder model with multi-level copy mechanisms to
generate such questions. Furthermore, the answer aware loss is introduced to
make generated questions corresponding to more definitive answers. Experiments
demonstrate that our model achieves state-of-the-art performance. Meanwhile,
such generated question can express the given predicate and correspond to a
definitive answer.
