Data Manipulation: Towards Effective Instance Learningfor Neural Dialogue Generation via Learning to Augment and Reweight
Table 6: Model ablation test (%) on DailyDialog.
['[EMPTY]', 'Dist-1', 'Dist-2', 'Dist-3', 'Intra-1', 'Intra-2', 'Intra-3', 'Ent-1', 'Ent-2', 'Ent-3', 'BLEU', 'Avg', 'Ext', 'Gre']
Among different variants, without data augmentation, the performance degrades rapidly. Meanwhile, without weighting or instance filter also decreases the performance. This implies that the neural dialogue generation model not only benefits from more training samples but also reaps greater advantages from those effective rather than inefficient instances.