We propose a machine comprehension model that learns jointly to generate questions and answers based on documents. The proposed model\u00a0uses a sequence-to-sequence framework that encodes the document and generates\u00a0a question (answer) given an answer (question). Significant improvement in\u00a0question-answering performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the\u00a0model benefits from jointly learning to perform both tasks. We believe the joint model offers a new perspective on machine comprehension beyond architectural engineering, and serves as a first step towards autonomous information seeking.<\/p>\n","protected":false},"excerpt":{"rendered":"
We propose a machine comprehension model that learns jointly to generate questions and answers based on documents. The proposed model\u00a0uses a sequence-to-sequence framework that encodes the document and generates\u00a0a question (answer) given an answer (question). Significant improvement in\u00a0question-answering performance is observed empirically on the SQuAD corpus, confirming our hypothesis that the\u00a0model benefits from jointly learning 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