CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation
Table 3: Automatic evaluation results for content and emotion measurements. The metrics Average, Extrema, Greedy, Coherence, Emotion-acc and Emotion-word are abbreviated as Avg., Ext., Gre., Coh., Emo-acc. and Emo-word., respectively.
['Method', 'Embedding Metrics Avg.', 'Embedding Metrics Ext.', 'Embedding Metrics Gre.', 'Embedding Metrics Coh.', 'Diversity Dist-1', 'Diversity Dist-2', 'BLEU Scores BLEU-1', 'BLEU Scores BLEU-2', 'Emotion Expression Emo-acc.', 'Emotion Expression Emo-word.']
The top part is the results of all baseline models, and we can see that CDL outperforms the other methods on all metrics (t-test, p-value < 0.05). The improvements of CDL on Coherence, Emotion-acc and Emotion-word are significant, indicating that it can enhance content consistency and emotion expression simultaneously. EmoDS and ECM have similar performance, as both of them use the forward method to pay more attention on the emotion factor. S2S-Attn can only generate fluent responses based on semantic mapping, but fail to express diverse responses. Comparisons among CDL-emo, CDL-con and CDL show the effectiveness of the combined reward for both emotion expression and content consistency. In addition, we can find that with the support of curriculum learning, CDL can achieve better results than CDL-DL.