Name
Abstract | ||
|---|---|---|
This paper presents a large-scale corpus for non-task-oriented dialogue response selection, which contains over 27K distinct prompts more than 82K responses collected from social media. To annotate this corpus, we define a 5-grade rating scheme: bad, mediocre, acceptable, good, and excellent, according to the relevance, coherence, informativeness, interestingness, and the potential to move a conversation forward. To test the validity and usefulness of the produced corpus, we compare various unsupervised and supervised models for response selection. Experimental results confirm that the proposed corpus is helpful in training response selection models. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computation and Language | Social media,Conversation,Computer science,Coherence (physics),Natural language processing,Artificial intelligence,Task oriented |
DocType | Volume | Citations |
Journal | abs/1805.05542 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Li | 1 | 52 | 43.73 |
| Yan Song | 2 | 284 | 53.62 |
| Haisong Zhang | 3 | 15 | 8.00 |
| Shuming Shi | 4 | 620 | 58.27 |