Name
Abstract | ||
|---|---|---|
With the development of information technology, it becomes more and more difficult to retrieve information from the internet for users. Question Answering (QA) is one of the methods to solve this problem. The users type natural language questions and get answers in QA systems. However, most QA systems only return a word or several words to the user, which is not friendly enough. The users are more willing to receive not only answers but also additional introductions or reasons. In this work, we propose a Nature Language Question Answering system which utilizes Seq2Seq model and Generative Adversarial Network (GAN) to generate answers with more information for users. To our best knowledge, this is the first work generating natural language answers in Question Answering domain. Our experiment results show NLQA can generate readable answers for users. © 2018 IEEE. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICSAI.2018.8599337 | 2018 5th International Conference on Systems and Informatics, ICSAI 2018 |
Keywords | Field | DocType |
Generative Adversarial Network,Natural Language Generation,Question Answering | Natural language generation,World Wide Web,Generative adversarial network,Question answering,Computer science,Information technology,Control engineering,Natural language,The Internet | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Chen Jin-Yuan | 2 | 22 | 3.27 |
| Fu Zuo-You | 3 | 0 | 0.34 |
| Zihan Xu | 4 | 4 | 2.09 |
| Zhao Cong-Zhi | 5 | 6 | 2.19 |