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The speaker-follower models have proven to be effective invision-and-language navigation, where a speaker model is used to synthesize newinstructions to augment the training data for a follower navigation model.However, in many of the previous methods, the generated instructions are notdirectly trained to optimize the performance of the follower. In this paper, wepresent \textsc{foam}, a \textsc{Fo}llower-\textsc{a}ware speaker\textsc{M}odel that is constantly updated given the follower feedback, so thatthe generated instructions can be more suitable to the current learning stateof the follower. Specifically, we optimize the speaker using a bi-leveloptimization framework and obtain its training signals by evaluating thefollower on labeled data. Experimental results on the Room-to-Room andRoom-across-Room datasets demonstrate that our methods can outperform strongbaseline models across settings. Analyses also reveal that our generatedinstructions are of higher quality than the baselines.

FOAM: A Follower-aware Speaker Model For Vision-and-Language Navigation