Guiding Variational Response Generator to Exploit Persona
Table 3: Comparison of different approaches on the Cornell Movie Dialogues corpus.
['Methods', 'BLEU', 'Embedding Average', 'Embedding Extreme', 'Embedding Greedy', 'Persona Metrics  [ITALIC] uRank', 'Persona Metrics  [ITALIC] uPPL', 'Persona Metrics  [ITALIC] uDist-1', 'Persona Metrics  [ITALIC] uDist-2']
The models that are aware of the specified user outperform others slightly on BLEU and Embedding metrics. Regards to persona metrics, the experimental results on Cornell corpus shows two main differences: a) The Speaker Model does not perform that well on user language style detection and generation, mainly because the training data of each user is less than that in Douban corpus. It is hard to automatically model the informative user embedding via target oriented learning without guidance. By contrast, utilizing the KL divergence as the guidance in CVAE effectively improves the experimental results. b) Due to the individual characteristics of movie characters, the user-embedding-enhanced models generate more diverse responses for different users, specially PAGenerator.