Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
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Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation (VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals. Despite recent progress, current research leaves unclear how much of a role language understanding plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions. Here, we highlight shortcomings of current metrics for the Room-to-Room dataset Anderson:2018:VLN and propose a new metric, Coverage weighted by Length Score. We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths. We join existing short paths to create more challenging extended paths, and show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.
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