Negative Training for Neural Dialogue Response Generation
Table 4: Main results of negative training with different rthres, for the frequent response problem. Diversity metrics for the responses in the test data are also shown, “E-n”/“M-ratio” refer to the Ent-n/max-ratio metric.
['Ubuntu', '[ITALIC] rthres', '[BOLD] PPL', '[BOLD] M-ratio', '[BOLD] E-2', '[BOLD] E-3']
The experiment with best diversity result and non-degenerate sample quality are shown in bold. We first observe a large gap on the diversity measures between the baseline models and the test set, especially on Switchboard and OpenSubtitles data. That indicates the severity of the frequent/generic response problem. For example, max-ratio is reduced by 73.7% and Ent-3 is increased by 149% for Switchboard data. Further, consistent improvement is achieved when a smaller rthres is used. However, sample quality will decrease (becoming too long or ungrammatical) when rthres is too small. The reason could be that when too much diversity is asked for, the model will go to extremes to provide diversity, resulting in degradation of sample quality.