DiffuCOMET: Contextual Commonsense Knowledge Diffusion
Silin Gao Mete Ismayilzada Mengjie Zhao Hiromi Wakaki Yuki Mitsufuji Antoine Bosselut
Inferring contextually-relevant and diverse commonsense to understand
narratives remains challenging for knowledge models. In this work, we develop a
series of knowledge models, DiffuCOMET, that leverage diffusion to learn to
reconstruct the implicit semantic connections between narrative contexts and
relevant commonsense knowledge. Across multiple diffusion steps, our method
progressively refines a representation of commonsense facts that is anchored to
a narrative, producing contextually-relevant and diverse commonsense inferences
for an input context. To evaluate DiffuCOMET, we introduce new metrics for
commonsense inference that more closely measure knowledge diversity and
contextual relevance. Our results on two different benchmarks, ComFact and
WebNLG+, show that knowledge generated by DiffuCOMET achieves a better
trade-off between commonsense diversity, contextual relevance and alignment to
known gold references, compared to baseline knowledge models.