Multi-source domain adaptation with mixture of experts

• Main Author: Shah, Darsh J.(Darsh Jaidip)
• Other Authors: Regina Barzilay.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2019
• Subjects: Electrical Engineering and Computer Science.
• Online Access: https://hdl.handle.net/1721.1/121741

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 35-37). === 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. === by Darsh J. Shah. === S.M. === S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science