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Found 7 papers, 6 papers with code
Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning
Spatial reasoning over text is challenging as the models not only need to extract the direct spatial information from the text but also reason over those and infer implicit spatial relations.
GLUECons: A Generic Benchmark for Learning Under Constraints
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models.
Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning
Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains.
Generalizable Neuro-symbolic Systems for Commonsense Question Answering
This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks.
SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM).
SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning
This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM).
Latent Alignment of Procedural Concepts in Multimodal Recipes
We propose a novel alignment mechanism to deal with procedural reasoning on a newly released multimodal QA dataset, named RecipeQA.
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