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- W2983331060 abstract "Understanding common sense is important for effective natural language reasoning. One type of common sense is how two objects compare on physical properties such as size and weight: e.g., ‘is a house bigger than a person?’. We probe whether pre-trained representations capture comparisons and find they, in fact, have higher accuracy than previous approaches. They also generalize to comparisons involving objects not seen during training. We investigate how such comparisons are made: models learn a consistent ordering over all the objects in the comparisons. Probing models have significantly higher accuracy than those baseline models which use dataset artifacts: e.g., memorizing some words are larger than any other word." @default.
- W2983331060 created "2019-11-22" @default.
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- W2983331060 date "2019-01-01" @default.
- W2983331060 modified "2023-09-27" @default.
- W2983331060 title "How Pre-trained Word Representations Capture Commonsense Physical Comparisons" @default.
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- W2983331060 doi "https://doi.org/10.18653/v1/d19-6016" @default.
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