Cross-Lingual Vision-Language Navigation
Table 2: Performance comparison for cross-lingual VLN models. All models are trained and tested with English and Chinese annotation data. Results are averaged over 3 runs.
['Model', '#params', '[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']
First, all cross-lingual models consistently perform better than the monolingual model (Base-mono), remarking the usefulness of cross-lingual learning for improving navigation policy. Second, sharing parameters can further assist knowledge transfer and thus boost navigation performance. Furthermore, although Shared Enc and Shared Enc-Dec produce similar results, Shared Enc-Dec is more efficient and more scalable since it yields a competitive good result with fewer parameters required, therefore we adopt Shared Enc-Dec for other experiments.