Name
Abstract | ||
|---|---|---|
In this poster, we propose a knowledge-enriched emotional conversation generation model (KE-EGM) that can ensure high quality content and focus on the impact of emotional factors during the conversation. First, we apply a multi-embedding fusion layer to provide this model with the token-level and sentence-level understanding. Then, the emotion flow attention mechanism combines flow emotion state and attention mechanism to learn and capture emotional information during the conversation dynamically. Finally, the multi-objective optimization mechanism is introduced to detect and generate fine-grained emotional responses. The experimental results show that KE-EGM outperforms several baselines not only in the content aspect but also in the emotional aspect.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3366424.3382693 | WWW '20: The Web Conference 2020
Taipei
Taiwan
April, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7024-0 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ao zhang | 1 | 6 | 7.89 |
| Shizhan Chen | 2 | 67 | 22.79 |
| Xiaowang Zhang | 3 | 163 | 38.77 |
| Rui Li | 4 | 0 | 0.34 |
| Xinzhi Zhang | 5 | 9 | 3.19 |