Condition-Transforming Variational AutoEncoder for Conversation Response Generation
Table 2: The objective diversity performance of different models.
['[EMPTY]', 'Seq2Seq', 'CVAE-simple', 'CVAE', 'CTVAE']
These two percentages, denoted as distinct-1 and distinct-2, respectively, represented the diversity at the n-gram level. We also counted the percentage of unique response sentences, which evaluated the diversity of responses at sentence level. It can be found that the Seq2Seq model had the worst diversity at both the n-gram level and the sentence level. CVAE performed slightly better than CVAE-simple. And both CVAE models achieved better diversity at the n-gram level than that of the CTVAE model, especially for distinct-2. For the diversity at sentence level, the percentage of unique responses achieved by CTVAE was close to that of CVAE models.