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We present a model that jointly learns the denotations of words together withtheir groundings using a truth-conditional semantics. Our model builds on theneurosymbolic approach of Mao et al. (2019), learning to ground objects in theCLEVR dataset (Johnson et al., 2017) using a novel parallel attentionmechanism. The model achieves state of the art performance on visual questionanswering, learning to detect and ground objects with question performance asthe only training signal. We also show that the model is able to learn flexiblenon-canonical groundings just by adjusting answers to questions in the trainingset.

Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention