Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network
Table 1: Summary of the two conversation emotion datasets: IEMOCAP, and MELD.
['Dataset', 'Fold', 'No. of Utt', 'No. of Conv', 'Avg. length of Conv']
Utterance Readers. We argue that the BiGRU is a better reader for utterances. Here, we test UniF-BiAGRU and BiF-AGRU on IEMOCAP with the utterance reader replaced by a 1-D CNN and a bidirectional LSTM (BiLSTM), respectively. For BiLSTM and BiGRU, the latter one performs better because GRUs are usually more powerful than LSTMs on small datasets. These results indicate that RNNs are more compatible in this Hierarchical Memory Network. Moreover, as BiGRU, BiLSTM attains better performance in BiF-AGRU than in UniF-BiAGRU, suggesting the advantage of BiF. In addition, we also conduct experiments for the CNN reader with Soft Attention, named as CNNsoft, which encounters the degradation of performance. It demonstrates that the improvement of our models is not achieved by only the BiGRU reader. Attention Choices & Memory Banks. We investigate the advantage of AGRU over Soft Attention here. The bidirectional variant of AGRU extends the advantage even further. It is noteworthy that we also include the results of Soft Attention with the BiF memory, which are considerably better than that with UniF. This further verifies that BiF can produce better memory representation. Context-Window Size. We plot the performance trend of UniF-BiAGRU and BiF-AGRU on both datasets when varying the context-window size K for building the memory bank. On both datasets, the two models follow similar trends On MELD, the best values of K are 5 and 10, respectively, which are much lower than that on IEMOCAP. We speculate that it is because the data of MELD comes from the Friends TV sitcom with more rapid emotion fluctuation. Therefore, a longer context may result in model confusion. In contrast, the emotion state in IEMOCAP evolves much more gently.