  The speaker-follower models have proven to be effective in
vision-and-language navigation, where a speaker model is used to synthesize new
instructions to augment the training data for a follower navigation model.
However, in many of the previous methods, the generated instructions are not
directly trained to optimize the performance of the follower. In this paper, we
present \textsc{foam}, a \textsc{Fo}llower-\textsc{a}ware speaker
\textsc{M}odel that is constantly updated given the follower feedback, so that
the generated instructions can be more suitable to the current learning state
of the follower. Specifically, we optimize the speaker using a bi-level
optimization framework and obtain its training signals by evaluating the
follower on labeled data. Experimental results on the Room-to-Room and
Room-across-Room datasets demonstrate that our methods can outperform strong
baseline models across settings. Analyses also reveal that our generated
instructions are of higher quality than the baselines.
