Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation
Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set. For “- Hierachical-Attn”, we remove the residual connections between the attention modules in the CMR decoders and all the attention memory access are based on the output from the LSTM. For “- MLP”, we further replace the MLP with a single linear layer with the non-linear activation.
['[BOLD] Model', '[BOLD] Joint Acc.']
Table 4: The ablation study on the WoZ2.0 dataset with the joint goal accuracy on the test set. [CONTINUE] The effectiveness of our hierarchical attention design is proved by an accuracy drop of 1.95% after removing residual connections and the hierarchical stack of our attention modules.