Modality-Balanced Models for Visual Dialogue
Table 6: The effect of round dropout: applying round dropout improves model’s performance on NDCG by around 1.2 while also improving other metrics. (CA: cross-attention model (base model), RD: round dropout).
['Models', 'NDCG', 'MRR', 'R@1', 'R@5', 'R@10', 'Mean']
A possible interpretation is that round dropout could help the model avoid from over-fitting to some patterns in the history features by intentionally dropping some of the features in the training session.