A Unified Query-based Generative Model for Question Generation and Question Answering

We propose a query-based generative model for solving both tasks of question generation (QG) and question answering (QA). The model follows the classic encoderdecoder framework. The encoder takes a passage and a query as input then performs query understanding by matching the query with the passage from multiple perspectives. 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 problem resulted from sequence learning with cross-entropy loss. For the QG task, our experiments show higher performances than the state-of-the-art results. When used as additional training data, the automatically generated questions even improve the performance of a strong extractive QA system. In addition, our model shows better performance than the state-of-the-art baselines of the generative QA task.