UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues
Table 5: Ablation analysis on the MultiWOZ2.1 in DST (top), context-to-text (middle), and end-to-end (bottom).
['[ITALIC] Xctx', '[ITALIC] Bt−1', '[ITALIC] Ndst [ITALIC] S', '[ITALIC] NdstD', '[ITALIC] Ngen', 'Lact', '[ITALIC] d', '[ITALIC] hatt', '[BOLD] Joint Acc.', '[BOLD] Slot Acc.', '[BOLD] Inform', '[BOLD] Success', '[BOLD] BLEU']
Ablation. Using the dual architecture also improves the latency in each attention layers as typically ∥D∥+∥S∥≪∥DS∥. (2) Using Bt−1 and only the last user utterance as the dialogue context performs as well as using Bt−1 and a full-length dialogue history. Using only the last user utterance, however, reduces the training time as the number of tokens in a full-length dialogue history is much larger than that of a dialogue state (particularly as the conversation evolves over many turns). (3) We note that removing the loss function to learn the dialogue act latent variable can hurt the generation performance. This reveals the benefit of enforcing a semantic condition on each token of the target response to steer the conversation towards the right direction for task completion. (4) In both state tracker and response generator modules, we note that learning feature representations through deeper attention networks can improve the quality of predicted states and system responses. This is consistent with our DST performance as compared to baseline models of shallow networks. (5) Lastly, our model achieves better performance as the number of attention heads increases, by learning more high-resolution dependencies.