Data Manipulation: Towards Effective Instance Learningfor Neural Dialogue Generation via Learning to Augment and Reweight
Table 2: Automatic evaluation results (%) on (a) DailyDialog and (b) OpenSubtitles. “★” denotes that the model is trained using our proposed data manipulation framework. The metrics Average, Extrema and Greedy are abbreviated as Avg, Ext and Gre, respectively. The best results in each group are highlighted with bold.
['[EMPTY]', 'Models', 'Dist-1', 'Dist-2', 'Dist-3', 'Intra-1', 'Intra-2', 'Intra-3', 'Ent-1', 'Ent-2', 'Ent-3', 'BLEU', 'Avg', 'Ext', 'Gre']
To investigate the effectiveness and general applicability of the proposed framework, we instantiate our data manipulation framework on several state-of-the-art models for dialogue generation. Compared with the vanilla training procedure, the proposed data manipulation framework brings solid improvements for all the three architectures regarding almost all the evaluation metrics. Such improvements are consistent across both two conversation datasets, affirming the superiority and general applicability of our proposed framework.