Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations
Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used to analyze user behaviors and detect fake news. Many recent ERC methods use graph-based neural networks to take the relationships between the utterances of the speakers into account. In particular, the state-of-the-art method considers self- and inter-speaker dependencies in conversations by using relational graph attention networks (RGAT). However, graph-based neural networks do not take sequential information into account. In this paper, we propose relational position encodings that provide RGAT with sequential information reflecting the relational graph structure. Accordingly, our RGAT model can capture both the speaker dependency and the sequential information. Experiments on four ERC datasets show that our model is beneficial to recognizing emotions expressed in conversations. In addition, our approach empirically outperforms the state-of-the-art on all of the benchmark datasets.
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Datasets
IEMOCAP
DailyDialog
MELD
EmoryNLP
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | DailyDialog | RGAT-ERC | Micro-F1 | 54.31 | # 17 | |
| Emotion Recognition in Conversation | EmoryNLP | RGAT-ERC | Weighted-F1 | 34.42 | # 25 | |
| Emotion Recognition in Conversation | IEMOCAP | RGAT-ERC | Weighted-F1 | 65.28 | # 38 | |
| Emotion Recognition in Conversation | MELD | RGAT-ERC | Weighted-F1 | 60.91 | # 44 |
