A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation
Table 1: The results of human annotations (C = Consistency, L = Logic, E = Emotion).
['Method', '[BOLD] Overall C', '[BOLD] Overall L', '[BOLD] Overall E', 'Happy C', 'Happy L', 'Happy E', 'Like C', 'Like L', 'Like E', 'Surprise C', 'Surprise L', 'Surprise E']
Overall, E-SCBA outperforms S2S-AW on all three metrics, where the compound information plays a positive role in the comprehensive promotion. However, in Surprise and Angry, the grades of Consistency and Logic are not satisfactory, since the data for them are much less than others (Surprise (1.2%) and Angry (0.7%)). Besides, the score of Emotion in Surprise has a big difference from others. We think the reason is that the characteristic of Surprise overlaps with other categories that have much more data, such as Happy, which interferes with the learning efficiency of the approach in Surprise. Meanwhile, it is harder for annotators to determine which one is the right emotion.