StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
What it is
This repository provides the dataset and PyTorch code for the AAAI 2022 paper on StepGame, a benchmark for evaluating multi-hop spatial reasoning in text. It includes a memory-augmented neural network baseline and scripts to generate additional synthetic samples with varying levels of noise.
The problem it solves
Lack of a robust, large-scale benchmark specifically designed to test multi-hop spatial reasoning capabilities in natural language processing models.
What you could use it for
- Benchmarking NLP models on spatial reasoning tasks
- Training and evaluating models for multi-hop question answering
- Researching robustness of language models to noisy spatial descriptions
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.