Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings (2024.findings-acl)
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| Challenge: | Concept embeddings are a useful and efficient mechanism for injecting commonsense knowledge into downstream tasks. |
| Approach: | They propose to model commonalities in concepts by capturing a more diverse range of commonsense properties. |
| Outcome: | The proposed model captures a more diverse range of commonsense properties and improves ontology completion and ultra-fine entity typing tasks. |
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| Challenge: | Pre-trained language models can capture commonsense properties that are rarely expressed in text. |
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Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
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| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
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| Challenge: | Contextualised Language Models (LMs) improve on word embeddings by encoding meaning of words in context. |
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An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)
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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
| Challenge: | a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks. |
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