Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
Table 3: Ablation tests on the SMD test set.
['[BOLD] Model', '[BOLD] Entity F1 (%)  [BOLD] Test', '[BOLD] Entity F1 (%)  [BOLD] Δ']
In detail, 1) w/o Domain-shared Knowledge Transfer denotes that we remove domain-shared feature and just keep fused domain-specific feature for generation. 2) w/o Domain Fusion mechanism denotes that we simply sum all domain-specific features rather than use the MOE mechanism to dynamically fuse domain-specific features. 3) w/o Multi-Encoder represents that we remove multi-encoder module and adopt one shared encoder in our framework. 4) w/o Multi-Decoder represents that we remove the multi-decoder module and adopt one shared decoder. 5) w/o Adversarial Training denotes that we remove the adversarial training in experimental setting. Generally, all the proposed components are effective to contribute the final performance. Specifically, we can clearly observe the effectiveness of our dynamic fusion mechanism where w/o domain-specific knowledge fusion causes 1.8% drops and the same trend in removing domain-shared knowledge fusion. This further verifies that domain-shared and domain-specific feature are benefit for each domain performance.