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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-445488","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Learning to generate natural 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