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- W4384890881 abstract "Neural knowledge models emerged and advanced common-sense-centric knowledge grounding. They parameterize a small seed curated commonsense knowledge graph (CS-KG) in a language model to generalize more. A current trend is to scale the seed up by directly mixing multiple sources of CS-KG (e.g., ATOMIC, ConceptNet) into one model. But, such brute-force mixing inevitably hinders effective knowledge consolidation due to i) ambiguous, polysemic, and/or inconsistent relations across sources and ii) knowledge learned in an entangled manner despite distinct types (e.g., causal, temporal). To mitigate this, we adopt a concept of commonsense knowledge dimension and propose a brand-new dimension-disentangled knowledge model (D2KM) learning paradigm with multiple sources. That is, a generative language model with dimension-specific soft prompts is trained to disentangle knowledge acquisitions along with different dimensions and facilitate potential intra-dimension consolidation across CS-KG sources. Experiments show our knowledge model outperforms its baselines in both standard and zero-shot scenarios." @default.
- W4384890881 created "2023-07-21" @default.
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- W4384890881 date "2023-07-18" @default.
- W4384890881 modified "2023-10-16" @default.
- W4384890881 title "Dimension-Prompts Boost Commonsense Consolidation" @default.
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- W4384890881 doi "https://doi.org/10.1145/3539618.3591973" @default.
- W4384890881 hasPublicationYear "2023" @default.
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