Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Table 6: Manual evaluation of the generated responses in terms of Content (Cont.) and Emotion (Emot.) .
['Method', 'Overall Cont.', 'Overall Emot.', 'Like Cont.', 'Like Emot.', 'Sad Cont.', 'Sad Emot.', 'Disgust Cont.', 'Disgust Emot.', 'Angry Cont.', 'Angry Emot.', 'Happy Cont.', 'Happy Emot.']
ECM with all options outperforms the other methods in both metrics significantly (2-tailed t-test, p<0.05 for Content, and p<0.005 for Emotion). After incorporating the internal memory and the external memory modules, the performance of ECM in Emotion is improved comparing to Emb, indicating our model can generate more explicit expressions of emotion. Besides, the performance in Content is improved from 1.256 of Emb to 1.299 of ECM, which shows the ability of ECM to control the weight of emotion and generate responses appropriate in content.