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• W2890407764 abstract "In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun–noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations." @default.
• W2890407764 created "2018-09-27" @default.
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• W2890407764 date "2018-01-01" @default.
• W2890407764 modified "2023-09-27" @default.
• W2890407764 title "Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation" @default.
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• W2890407764 doi "https://doi.org/10.18653/v1/d18-1178" @default.
• W2890407764 hasPublicationYear "2018" @default.
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