StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts


  • Zhengxiang Shi University College London
  • Qiang Zhang Zhejiang University
  • Aldo Lipani University College London



Speech & Natural Language Processing (SNLP), Knowledge Representation And Reasoning (KRR), Machine Learning (ML), Humans And AI (HAI)


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-step 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.




How to Cite

Shi, Z., Zhang, Q., & Lipani, A. (2022). StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11321-11329.



AAAI Technical Track on Speech and Natural Language Processing