Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Table 3: Automatic evaluation results (%) on the test set of three datasets: (a) PersonaChat, (b) DailyDialog and (c) OpenSubtitles. “▲” denotes that the model is trained using our proposed framework. The metrics Average, Extrema, Greedy and Coherence are abbreviated as Avg, Ext, Gre and Coh, respectively. The best results in each group are highlighted with bold.
['[EMPTY]', 'Models', 'BLEU', 'Dist-1', 'Dist-2', 'Dist-3', 'Intra-1', 'Intra-2', 'Intra-3', 'Avg', 'Ext', 'Gre', 'Coh', 'Ent-1', 'Ent-2']
Compared with the vanilla training procedure, our curriculum learning framework 1) brings solid improvements for all the five dialogue models regarding almost all the evaluation metrics, 2) achieves competitive performance across three datasets, affirming the superiority and general applicability of our proposed framework. We also notice that the relative improvements of Distinct on OpenSubtitles are much larger (up to 122.46%) than the other two experiment datasets. We conjecture that the OpenSubtitles, with extremely uneven-complexity dialogue samples, benefits more from the multi-curricula learning paradigm.