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Title: SpartQA: A Textual Question Answering Benchmark for Spatial Reasoning
This paper proposes a question-answering (QA) benchmark for spatial reasoning on nat- ural language text which contains more real- istic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). We propose a distant supervision method to improve on this task. Specifically, we design grammar and reason- ing rules to automatically generate a spatial de- scription of visual scenes and corresponding QA pairs. Experiments show that further pre- training LMs on these automatically generated data significantly improves LMs’ capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ. We hope that this work can foster inves- tigations into more sophisticated models for spatial reasoning over text.  more » « less
Award ID(s):
2028626
PAR ID:
10227086
Author(s) / Creator(s):
Date Published:
Journal Name:
The 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2021)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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