{"type":"ScholarlyArticle","title":"Neural Generative Question Answering","authors":[{"name":"Jun Yin","slug":"jun-yin","given":"Jun","family":"Yin","affiliation":""},{"name":"Xin Jiang","slug":"xin-jiang","given":"Xin","family":"Jiang","affiliation":""},{"name":"Zhengdong Lu","slug":"zhengdong-lu","given":"Zhengdong","family":"Lu","affiliation":""},{"name":"Lifeng Shang","slug":"lifeng-shang","given":"Lifeng","family":"Shang","affiliation":""},{"name":"Hang Li","slug":"hang-li-2","given":"Hang","family":"Li","affiliation":""},{"name":"Xiaoming Li","slug":"xiaoming-li","given":"Xiaoming","family":"Li","affiliation":""}],"abstract":"This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.","doi":null,"url":"https://ijcai.org/proceedings/2016/422","pdf":"https://ijcai.org/proceedings/2016/0422.pdf","markdown":"https://ijcai.org/proceedings/2016/422.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/422","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":422,"pages":{"first":2972,"last":2977},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}