StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
This addresses the need for more challenging spatial reasoning benchmarks in AI, though it is incremental as it builds on existing datasets like bAbI.
The authors tackled the problem of robust multi-hop spatial reasoning in natural language by introducing StepGame, a new benchmark dataset, and found that state-of-the-art models on the bAbI dataset struggled on it, while their proposed TP-MANN model outperformed all baselines with superior generalization and robustness.
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.