Conversational Transfer Learning for Emotion Recognition
Table 1: Table illustrates the sizes of the datasets used in this work. #D represents the number of dialogues whereas #U represents the total number of constituting utterances.
['[EMPTY]', 'Dataset', 'Dataset', 'Dataset splits train', 'Dataset splits validation', 'Dataset splits test']
For both IEMOCAP and SEMAINE, we generate the validation sets by random-sampling of 20% dialogue videos from the training sets. From the table, IEMOCAP is observed a fairly balanced dataset whereas DailyDialog is highly skewed towards sentences with no emotion. As such, we decide upon different metrics for each dataset as discussed next.