Inferring spatial relations in natural language is a crucial ability an
intelligent system should possess. The bAbI dataset tries to capture tasks
relevant to this domain (task 17 and 19). However, these tasks have several
limitations. Most importantly, they are limited to fixed expressions, they are
limited in the number of reasoning steps required to solve them, and they fail
to test the robustness of models to input that contains irrelevant or redundant
information. In this paper, we present a new Question-Answering dataset called
StepGame for robust multi-hop spatial reasoning in texts. Our experiments
demonstrate that state-of-the-art models on the bAbI dataset struggle on the
StepGame dataset. Moreover, we propose a Tensor-Product based Memory-Augmented
Neural Network (TP-MANN) specialized for spatial reasoning tasks. Experimental
results on both datasets show that our model outperforms all the baselines with
superior generalization and robustness performance.Comment: AAAI 2022 Camera Read
