Open Domain Dialogue Generation with Latent Images
Table 2: Evaluation results on automatic metrics. Numbers in bold indicate the best performing model on the corresponding metrics.
['Task', 'Model', 'PPL', 'BLEU-1', 'Rouge-L', 'Average', 'Extrema', 'Greedy', 'Dist-1', 'Dist-2']
In image-grounded conversation, ImgVAE significantly outperforms all baseline models on most metrics. Particularly, ImgVAE outperforms T&I and ImgRG even after their training is augmented with the Reddit data. The results indicate the effectiveness of the proposed approach on leveraging both multi-modal data and single-modal data for image-grounded dialogue generation. In text-based conversation, ImagVAE achieves comparable performance with the state-of-the-art deep transformer structure (i.e., ReCoSa) in terms of response relevance and PPL, but improves upon informativeness of responses with large margins. This is because latent images, when properly controlled by the gate in the response generator, can enhance appearance of informative content in responses, as will be further verified by human annotations and the analysis in Discussions.