Data Manipulation: Towards Effective Instance Learningfor Neural Dialogue Generation via Learning to Augment and Reweight
Table 3: Performance (%) of our approach instantiated on the naive SEQ2SEQ and the baseline approaches on (a) DailyDialog and (b) OpenSubtitles.
['[EMPTY]', 'Models', 'Dist-1', 'Dist-2', 'Dist-3', 'Intra-1', 'Intra-2', 'Intra-3', 'Ent-1', 'Ent-2', 'Ent-3', 'BLEU', 'Avg', 'Ext', 'Gre']
We further compare our model with existing related methods. In particular, the improvement on Distinct metrics of our model is much greater, which implies that data manipulation effectively induce the neural dialogue model generating more diverse responses.