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- W4385573998 abstract "Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning. In this paper, we propose to learn commonsense knowledge representation by MICO, a Multi-alternative contrastIve learning framework on COmmonsense knowledge graphs (MICO). MICO generates the commonsense knowledge representation by contextual interaction between entity nodes and relations with multi-alternative contrastive learning. In MICO, the head and tail entities in an (h,r,t) knowledge triple are converted to two relation-aware sequence pairs (a premise and an alternative) in the form of natural language. Semantic representations generated by MICO can benefit the following two tasks by simply comparing the similarity score between the representations: 1) zero-shot commonsense question answering tasks; 2) inductive commonsense knowledge graph completion tasks. Extensive experiments show the effectiveness of our method." @default.
- W4385573998 created "2023-08-05" @default.
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- W4385573998 date "2022-01-01" @default.
- W4385573998 modified "2023-09-24" @default.
- W4385573998 title "MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation" @default.
- W4385573998 doi "https://doi.org/10.18653/v1/2022.findings-emnlp.96" @default.
- W4385573998 hasPublicationYear "2022" @default.
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