{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-generative-question-answering","title":"Neural Generative Question Answering","arxiv_id":"1512.01337","date":"2015-12-04","proceeding":"WS 2016 6","authors":["Jun Yin","Xin Jiang","Zhengdong Lu","Lifeng Shang","Hang Li","Xiaoming Li"],"abstract":"This paper presents an end-to-end neural network model, named Neural\nGenerative Question Answering (GENQA), that can generate answers to simple\nfactoid questions, based on the facts in a knowledge-base. More specifically,\nthe model is built on the encoder-decoder framework for sequence-to-sequence\nlearning, while equipped with the ability to enquire the knowledge-base, and is\ntrained on a corpus of question-answer pairs, with their associated triples in\nthe knowledge-base. Empirical study shows the proposed model can effectively\ndeal with the variations of questions and answers, and generate right and\nnatural answers by referring to the facts in the knowledge-base. The experiment\non question answering demonstrates that the proposed model can outperform an\nembedding-based QA model as well as a neural dialogue model trained on the same\ndata.","url_abs":"http://arxiv.org/abs/1512.01337v4","url_pdf":"http://arxiv.org/pdf/1512.01337v4.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-generative-question-answering","repo_url":"https://github.com/jxfeb/Generative_QA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"generative-question-answering","task_name":"Generative Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1512.01337","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}