Open Domain Dialogue Generation with Latent Images
Table 3: Human evaluation results.
['Task', 'Models', 'Fluency', 'Relevance Text', 'Relevance Image', 'Richness', 'Kappa']
Basically, all models in both tasks can generate fluent and grammatical responses for most test input. In image-grounded conversation, ImgVAE outperforms all baselines in terms of context-relevance, image-relevance, and richness, which is consistent with the automatic evaluation results. In text-based conversation, ImgVAE significantly improves upon richness. which further demonstrates the effect of latent images. All kappa values exceed or close to 0.6, indicating substantial agreement among the annotators. For reference, we show two cases in supplementary material.