Cross-Lingual Vision-Language Navigation
Table 4: Ablation study for the meta-learner. Reported results are averages of 5 individual runs. ensemble is to assign equal weight to each language. meta-learner is our basic framework in Figure 1. Both results are reported on Chinese human instructions.
['Model', '[BOLD] Validation Seen PL', '[BOLD] Validation Seen NE \\downarrow', '[BOLD] Validation Seen SR \\uparrow', '[BOLD] Validation Seen SPL \\uparrow', '[BOLD] Validation Seen CLS \\uparrow', '[BOLD] Validation Unseen PL', '[BOLD] Validation Unseen NE \\downarrow', '[BOLD] Validation Unseen SR \\uparrow', '[BOLD] Validation Unseen SPL \\uparrow', '[BOLD] Validation Unseen CLS \\uparrow']
Meta-learner To validate the effectiveness of the meta-learner, we compare it with a simple ensemble, which assigns equal confidence to two languages at all time steps, without any learnable parameters. Our meta-leaner has higher performance on the validation unseen set, suggests that “learning to trust” is important for cross-lingual vision-language navigation.