  We propose a generative machine comprehension model that learns jointly to
ask and answer questions based on documents. The proposed model uses a
sequence-to-sequence framework that encodes the document and generates a
question (answer) given an answer (question). Significant improvement in model
performance is observed empirically on the SQuAD corpus, confirming our
hypothesis that the model benefits from jointly learning to perform both tasks.
We believe the joint model's novelty offers a new perspective on machine
comprehension beyond architectural engineering, and serves as a first step
towards autonomous information seeking.
