Multi-Source Domain Adaptation with Mixture of Experts
Jiang Hong Guo, Darsh Shah, Regina Barzilay · 2018
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 metatraining.Experimental results on sentiment analysis and part-of-speech tagging demonstrate that our approach consistently outperforms multiple baselines and can robustly handle negative transfer. 1