  Contrarily to humans who have the ability to recombine familiar expressions
to create novel ones, modern neural networks struggle to do so. This has been
emphasized recently with the introduction of the benchmark dataset "gSCAN"
(Ruis et al. 2020), aiming to evaluate models' performance at compositional
generalization in grounded language understanding. In this work, we challenge
the gSCAN benchmark by proposing a simple model that achieves surprisingly good
performance on two of the gSCAN test splits. Our model is based on the
observation that, to succeed on gSCAN tasks, the agent must (i) identify the
target object (think) before (ii) navigating to it successfully (act).
Concretely, we propose an attention-inspired modification of the baseline model
from (Ruis et al. 2020), together with an auxiliary loss, that takes into
account the sequential nature of steps (i) and (ii). While two compositional
tasks are trivially solved with our approach, we also find that the other tasks
remain unsolved, validating the relevance of gSCAN as a benchmark for
evaluating models' compositional abilities.
