Name
Abstract | ||
|---|---|---|
When conversational communication, people often draw upon their rich world knowledge in addition to the dialogue context. The commonsense world fact can facilitate natural language understanding. In the paper, we present a rich knowledge cognition hierarchical (RKC-H) multi-turn dialogue model in open-domain to improve language generation. Given the input, the model selects the corresponded seed-g... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TASE49443.2020.00013 | 2020 International Symposium on Theoretical Aspects of Software Engineering (TASE) |
Keywords | DocType | ISBN |
multi-turn dialogue system,knowledge graph,graph attention,hierarchical encoder | Conference | 978-1-7281-4086-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feifei Xu | 1 | 76 | 5.25 |
| Guanqun Ding | 2 | 7 | 2.86 |
| Wenkai Zhang | 3 | 0 | 0.34 |
| Audrey | 4 | 0 | 0.34 |