Name
Abstract | ||
|---|---|---|
•A novel framework estimates the relevance between dialogue contexts and responses.•The RAP-Net model integrates benefits from recurrent units, attention, and pooling.•The model is capable of generalize to different response selection datasets.•The proposed knowledge-grounding feature significantly improves the results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.csl.2020.101079 | Computer Speech & Language |
Keywords | DocType | Volume |
Attention,Dialogue modeling,Response selection,DSTC | Journal | 63 |
ISSN | Citations | PageRank |
0885-2308 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao-Wei Huang | 1 | 0 | 1.01 |
| Ting-Rui Chiang | 2 | 0 | 1.69 |
| Shang-Yu Su | 3 | 9 | 4.88 |
| Yun-Nung Chen | 4 | 324 | 35.41 |