  We present a model that jointly learns the denotations of words together with
their groundings using a truth-conditional semantics. Our model builds on the
neurosymbolic approach of Mao et al. (2019), learning to ground objects in the
CLEVR dataset (Johnson et al., 2017) using a novel parallel attention
mechanism. The model achieves state of the art performance on visual question
answering, learning to detect and ground objects with question performance as
the only training signal. We also show that the model is able to learn flexible
non-canonical groundings just by adjusting answers to questions in the training
set.
