Name
Title | ||
|---|---|---|
Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model. |
Abstract | ||
|---|---|---|
Using a sequence-to-sequence framework, many neural conversation models for chit-chat succeed in naturalness of the response. Nevertheless, the neural conversation models tend to give generic responses which are not specific to given messages, and it still remains as a challenge. To alleviate the tendency, we propose a method to promote message-relevant and diverse responses for neural conversation model by using self-attention, which is time-efficient as well as effective. Furthermore, we present an investigation of why and how effective self-attention is in deep comparison with the standard dialogue generation. The experiment results show that the proposed method improves the standard dialogue generation in various evaluation metrics. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computation and Language | Conversation,Computer science,Naturalness,Natural language processing,Artificial intelligence |
DocType | Volume | Citations |
Journal | abs/1805.08983 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonggu Kim | 1 | 0 | 0.68 |
| Doyeon Kong | 2 | 0 | 0.68 |
| Jong-Hyeok Lee | 3 | 740 | 97.88 |