Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
Table 7: Performance of our model with partial knowledge labels on Wizard of Wikipedia (Dinan et al., 2019).
['Method', '[BOLD] Test Seen PPL', '[BOLD] Test Seen R-1', '[BOLD] Test Seen R-2', '[BOLD] Test Seen Acc', '[BOLD] Test Unseen PPL', '[BOLD] Test Unseen R-1', '[BOLD] Test Unseen R-2', '[BOLD] Test Unseen Acc']
We attain better performance with more labeled knowledge data for training as expected. Furthermore, our model achieves competitive performance with less label. For instance, our model using only 1/4 labeled training data is comparable to E2E Transformer MemNet and even better in Test Unseen. As a result, our sequential latent knowledge selection model can be utilized in a semi-supervised method without severe drop in the performance.