Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
Table 4: Analysis of the relatedness-affectiveness tradeoff on the validation sets. Each column corresponds to a fixed λk for all concepts (see Equation 8).
['[BOLD] Dataset', '[BOLD] 0', '[BOLD] 0.3', '[BOLD] 0.7', '[BOLD] 1']
It is clear that λk makes a noticeable impact on the model performance. Discarding relatedness or affectiveness completely will cause significant performance drop on all datasets, with one exception of IEMOCAP. One possible reason is that conversations in IEMOCAP are emotional dialogues, therefore, the affectiveness factor in our proposed graph attention mechanism can provide more discriminative power.