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• W2891691791 abstract "We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources. The key idea is to explicitly capture the relationship between a target example and different source domains. This relationship, expressed by a point-to-set metric, determines how to combine predictors trained on various domains. The metric is learned in an unsupervised fashion using meta-training. Experimental results on sentiment analysis and part-of-speech tagging demonstrate that our approach consistently outperforms multiple baselines and can robustly handle negative transfer." @default.
• W2891691791 created "2018-09-27" @default.
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• W2891691791 date "2018-01-01" @default.
• W2891691791 modified "2023-10-10" @default.
• W2891691791 title "Multi-Source Domain Adaptation with Mixture of Experts" @default.
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• W2891691791 doi "https://doi.org/10.18653/v1/d18-1498" @default.
• W2891691791 hasPublicationYear "2018" @default.
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