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Compositional generalization, the ability of intelligent models toextrapolate understanding of components to novel compositions, is a fundamentalyet challenging facet in AI research, especially within multimodalenvironments. In this work, we address this challenge by exploiting thesyntactic structure of language to boost compositional generalization. Thispaper elevates the importance of syntactic grounding, particularly throughattention masking techniques derived from text input parsing. We introduce andevaluate the merits of using syntactic information in the multimodal groundingproblem. Our results on grounded compositional generalization underscore thepositive impact of dependency parsing across diverse tasks when utilized withWeight Sharing across the Transformer encoder. The results push thestate-of-the-art in multimodal grounding and parameter-efficient modeling andprovide insights for future research.

Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments