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arXiv · 2311.17280

Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?

Wang Zhu·Ishika Singh·Yuan Huang·Robin Jia·Jesse Thomason
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Abstract

Data augmentation via back-translation is common when pretraining Vision-and-Language Navigation (VLN) models, even though the generated instructions are noisy. But: does that noise matter? We find that nonsensical or irrelevant language instructions during pretraining can have little effect on downstream performance for both HAMT and VLN-BERT on R2R, and is still better than only using clean, human data. To underscore these results, we concoct an efficient augmentation method, Unigram + Object, which generates nonsensical instructions that nonetheless improve downstream performance. Our findings suggest that what matters for VLN R2R pretraining is the quantity of visual trajectories, not the quality of instructions.

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Wang Zhu, Ishika Singh, Yuan Huang, Robin Jia, Jesse Thomason. 2023-11-28. Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?. https://arxiv.org/abs/2311.17280

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