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Multi Source Domain Adaptation with Mixture of Experts

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Figure

Figure 1: Architecture of the MoE model. E is the en-coder which maps an input x to a hidden representationE(x); F Si is the classifier on the ith source domain; Dis the critic that is only used during adversarial train-ing
Table 1: Multi-Source domain adaptation accuracy on Amazon dataset of CHEN12. ∗ indicates negative transfer,i.e., the unified multi-source model underperforms the best single-source model
Figure 2: α distributions across source domains for randomly selected 200 examples in each target domain ofCHEN12 (left) and ZISER17 (right)
Table 4: POS tagging results on SANCL data. Source domains include Web, Emails, Twitter

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