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In this work, we propose to apply the neural\nencoder-decoder model to generate meaningful and diverse questions from natural\nlanguage sentences. The encoder reads the input text and the answer position,\nto produce an answer-aware input representation, which is fed to the decoder to\ngenerate an answer focused question. We conduct a preliminary study on neural\nquestion generation from text with the SQuAD dataset, and the experiment\nresults show that our method can produce fluent and diverse questions.","url_abs":"http://arxiv.org/abs/1704.01792v3","url_pdf":"http://arxiv.org/pdf/1704.01792v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-question-generation-from-text-a","repo_url":"https://github.com/magic282/NQG","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neural-question-generation-from-text-a","repo_url":"https://github.com/YuxiXie/Neural-Question-Generation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"neural-question-generation-from-text-a","repo_url":"https://github.com/gouqi666/rast","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neural-question-generation-from-text-a","repo_url":"https://github.com/neineis/multi-head-attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neural-question-generation-from-text-a","repo_url":"https://github.com/zeaver/multifactor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"neural-question-generation-from-text-a","repo_url":"https://github.com/iambabao/copynet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":null,"task_name":"Position"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"question-generation","task_name":"Question-Generation"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-generation-on-squad11","task":"Question Generation","dataset":"SQuAD1.1","model":"NQG++","rank_in_archive_order":13,"of":13,"metrics":{"BLEU-4":"13.27"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.01792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.01792"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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