TL;DR
This paper introduces a neuro-symbolic method for zero-shot commonsense question answering that dynamically constructs knowledge graphs on demand, improving reasoning and interpretability over static knowledge bases.
Contribution
It proposes a novel approach that generates contextually relevant symbolic knowledge structures using generative models, enabling reasoning in zero-shot scenarios.
Findings
Significant performance improvements over pretrained language models.
Effective dynamic knowledge graph construction for reasoning.
Provides interpretable reasoning paths for predictions.
Abstract
Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it. In this paper, we present initial studies toward zero-shot commonsense question answering by formulating the task as inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires commonsense knowledge integration where contextually relevant knowledge is often not present in existing knowledge bases. Therefore, we present a novel approach that generates contextually-relevant symbolic knowledge structures on demand using generative neural commonsense knowledge models.…
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