  We propose a query-based generative model for solving both tasks of question
generation (QG) and question an- swering (QA). The model follows the classic
encoder- decoder framework. The encoder takes a passage and a query as input
then performs query understanding by matching the query with the passage from
multiple per- spectives. The decoder is an attention-based Long Short Term
Memory (LSTM) model with copy and coverage mechanisms. In the QG task, a
question is generated from the system given the passage and the target answer,
whereas in the QA task, the answer is generated given the question and the
passage. During the training stage, we leverage a policy-gradient reinforcement
learning algorithm to overcome exposure bias, a major prob- lem resulted from
sequence learning with cross-entropy loss. For the QG task, our experiments
show higher per- formances than the state-of-the-art results. When used as
additional training data, the automatically generated questions even improve
the performance of a strong ex- tractive QA system. In addition, our model
shows bet- ter performance than the state-of-the-art baselines of the
generative QA task.
