In this paper, we argue that gradient descent is one of the reasons that make compositionality learning hard during neural network optimization. We find that the optimization process imposes a bias toward non-compositional solutions. This is caused by gradient descent, trying to use all available and redundant information from input, violating the conditional independence property of compositionality. Based on this finding, we suggest that compositionality learning approaches considering only model architecture design are unlikely to achieve complete compositionality. This is the first work to investigate the relation between compositional learning and gradient descent. We hope this study provides novel insights into compositional generalization, and forms a basis for new research directions to equip machine learning models with such skills for human-level intelligence.
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