Condition-Transforming Variational AutoEncoder for Conversation Response Generation
Table 1: The objective fluency performance of different models.
['[EMPTY]', 'Seq2Seq', 'CVAE-simple', 'CVAE', 'CTVAE']
3.4.1 Fluency We trained a RNN language model (LM) Furthermore, the percentage of generated responses that exactly matched any responses in the training set were counted. This matching percentage was used as a metric to evaluate the model’s ability to generate fluency sentences with reasonable syntactic and semantic representations. It can be found that the Seq2Seq model achieved the lowest perplexity on LM and the highest matching percentage because it tended to generate its dull, generic and repeated responses. The CVAE models performed worst on these two fluency metrics. For CTVAE, it performed much better than the CVAE models on both LM perplexity and matching percentage.