---
title: "Neural Generative Question Answering"
authors:
  - "Jun Yin"
  - "Xin Jiang"
  - "Zhengdong Lu"
  - "Lifeng Shang"
  - "Hang Li"
  - "Xiaoming Li"
doi: ""
url: "https://ijcai.org/proceedings/2016/422"
pdf: "https://ijcai.org/proceedings/2016/0422.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2972-2977"
track: ""
keywords:
---

# Neural Generative Question Answering

Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2972-2977.

## 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.