Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories.
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Datasets
ConceptNet
IEMOCAP
DailyDialog
MELD
EmoryNLP
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Emotion Recognition in Conversation | DailyDialog | TODKAT | Micro-F1 | 58.47 | # 12 | ||
| Weighted F1 | 52.56 | # 2 | |||||
| Emotion Recognition in Conversation | EmoryNLP | TODKAT | Weighted-F1 | 38.69 | # 15 | ||
| Micro-F1 | 42.38 | # 4 | |||||
| Emotion Recognition in Conversation | IEMOCAP | TODKAT | Weighted-F1 | 62.75 | # 47 | ||
| Accuracy | 63.4 | # 26 | |||||
| Macro-F1 | 60.66 | # 3 | |||||
| Emotion Recognition in Conversation | MELD | TODKAT | Weighted-F1 | 65.47 | # 25 |
