Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Table 5: Ablation test (%) for the proposed five curriculum learning attributes on the validation set of the DailyDialog dataset with the SEQ2SEQ model.
['Curriculum by', 'BLEU', 'Dist-1', 'Dist-2', 'Dist-3', 'Intra-1', 'Intra-2', 'Intra-3', 'Avg', 'Ext', 'Gre', 'Coh', 'Ent-1', 'Ent-2']
, we conduct the ablation test using the SEQ2SEQ model by only exploiting a single attribute during the curriculum learning. We observe that the curriculum learning leads to consistent performance improvements, even with one single conversational attribute. When applying the multi-curricula learning method to the model, we observe the nearly best performance.