Personalizing Dialogue Agents via Meta-Learning
Table 1: Results of automatic and human evaluation: PAML vs Dialogue+Persona shows the our approach can achieve good consistency by using few dialogues instead of conditioning on the persona description, PAML vs Dialogue+Fine-tuning shows the effectiveness of meta-learning approach in personalizing dialogue model.
['[EMPTY]', '[ITALIC]  [BOLD] Automatic  [BOLD] PPL', '[ITALIC]  [BOLD] Automatic  [BOLD] BLEU', '[ITALIC]  [BOLD] Automatic  [ITALIC]  [BOLD] C', '[ITALIC]  [BOLD] Human  [BOLD] Fluency', '[ITALIC]  [BOLD] Human  [BOLD] Consistency']
PAML achieve consistently better results in term of dialogue consistency in both automatic and human evaluation. The latter also shows that all the experimental settings have comparable fluency scores, where instead perplexity and BLEU score are lower in PAML. This confirms that these measures are not correlated to human judgment Liu et al. For completeness, we also show generated responses examples from PAML and baseline models in Appendix.