Data Manipulation: Towards Effective Instance Learningfor Neural Dialogue Generation via Learning to Augment and Reweight
Table 5: Ablation test (%) for word-level and sentence-level augmentations.
['[EMPTY]', 'Dist-1', 'Dist-2', 'Dist-3', 'Intra-1', 'Intra-2', 'Intra-3', 'Ent-1', 'Ent-2', 'Ent-3', 'BLEU', 'Avg', 'Ext', 'Gre']
In our framework, we implement two kinds of augmentation mechanisms. Word-level augmentation enriches the given samples by substituting words, while sentence-level augmentation paraphrases the original samples through back-translation. Both augmentation mechanisms improve the performance over the vanilla SEQ2SEQ baseline, while sentence-level augmentation performs slightly better than word-level augmentation on most evaluation metrics. One possible reason is that sentence-level augmentation captures more paraphrasing phenomenon.