Multi-View Attention Networks for Visual Dialog
Table 3: Ablations of our approaches on the VisDial v1.0 validation dataset.
['Model', 'context-level history', 'topic-level history', 'NDCG', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean']
We conducted ablation studies on the VisDial v1.0 validation splits to evaluate the influence of each component in our model. Modality Fusion module is not ablated because this module handles the visual features. We use the same discriminative decoder model Das et al. for all ablations to exclude the impact of multitask learning. Both models obtain slightly lower performance with respect to all evaluation metrics than the MVAN model. We can hence infer that the two modules are complementary with respect to each other and our model integrates these complementary characteristics well for the task. Recent approaches Murahari et al. Specifically, adding dialog history features improves the MRR score by 3.54% on average, whereas NDCG score is decreased by 1.92% on average. We observe that the model has a tendency to predict the answers more precisely (i.e., it has a better MRR score) when the dialog history features are added. This may imply that question-related clues in the dialog history are important factors in reasoning the ground truth, but they hinder the model’s generalization ability (i.e., they lower the NDCG score).