Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
Table 2: Quantitative results on the Wizard of Wikipedia dataset (Dinan et al., 2019). The method with [∗] does not use the knowledge loss. The scores of E2E Transformer MemNet† and Transformer (no knowledge)† are from the original paper. The variant (BERT vocab)‡ is re-runned using the authors’ code, since the vocabulary is different from original paper due to the use of BERT.
['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']
As one way to take advantage of true labels for training of latent models, prior research has employed auxiliary losses over latent variables ( Similarly, we use the knowledge loss from Dinan et al. (i.e. the cross-entropy loss between predicted and true knowledge sentences) as an auxiliary loss for the latent variable. Thus, the training objective is a combination of the variational lower-bound from Eq. [ Eqϕ(kt)[logpθ(yt|x≤t,y