Grounded Compositional Generalization with Environment Interactions

In this paper, we present a compositional generalization approach in grounded agent instruction learning. Compositional generalization is an important part of human intelligence, but current neural network models do not have such ability. This is more complicated in multi-modal problems with grounding. Our proposed approach has two main ideas. First, we use interactions between agent and the environment to find components in the output. Second, we apply entropy regularization to learn corresponding input components for each output component. The results show the proposed approach significantly outperforms baselines in most tasks, with more than 25% absolute average accuracy increase. We also investigate the impact of entropy regularization and other changes with ablation study. We hope this work is the first step to address grounded compositional generalization, and it will be helpful in advancing artificial intelligence research.