ShiZhengyan/StepGame
[AAAI 2022] Dataset and pytorch codes for the paper titled "StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts" in AAAI 2022 (Oral)
This project offers a specialized dataset and accompanying code for evaluating how well AI models can understand and reason about spatial relationships described across multiple sentences in text. It takes text-based stories and questions about spatial arrangements as input, and outputs labels indicating correct spatial reasoning. This is primarily useful for AI researchers and natural language processing engineers who are developing and testing advanced AI systems.
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Use this if you are developing or benchmarking AI models that need to perform complex spatial reasoning across several pieces of information within a text.
Not ideal if you are looking for a pre-trained model to directly apply to a business problem, as this is a research benchmark.
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32
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7
Language
Python
License
MIT
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Last pushed
Mar 20, 2024
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