Name
Abstract | ||
|---|---|---|
Real-time emotion recognition in conversations (RTERC), the task of using the historical context to identify the emotion of a query utterance in a conversation, is important for opinion mining and building empathetic machines. Existing works mainly focus on obtaining each utterance representation separately and then utilizing utterance-level features to model the emotion representation of the query. These approaches treat each utterance as a unit and capture the utterance-level dependencies in the context, but ignore the word-level dependencies among different utterances. In this paper, we propose a multi-view network (MVN) to explore the emotion representation of a query from two different views, i.e., word- and utterance-level views. For the word-level view, MVN takes the context and query as word sequences and then models the word-level dependencies among utterances. For the utterance-level view, MVN extracts each utterance representation separately and then models the utterance-level dependencies in the context. Experimental results on two public emotion conversation datasets show that the proposed model outperforms the state-of-the-art baselines. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2021.107751 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Emotion recognition,Real-time conversations,Multi-view learning,Word-level dependencies,Utterance-level dependencies | Journal | 236 |
ISSN | Citations | PageRank |
0950-7051 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Ma | 1 | 1 | 0.36 |
| Jian Wang | 2 | 73 | 16.74 |
| Hongfei Lin | 3 | 1 | 0.70 |
| Xuejun Pan | 4 | 5 | 2.55 |
| Yijia Zhang | 5 | 113 | 14.67 |
| Zhihao Yang | 6 | 270 | 36.04 |