Multi-source domain adaptation with mixture of experts
Name
1102051083-MIT.pdf
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1.66 MB
Format
Adobe PDF
Checksum (MD5)
adca1953570ae219c96720ba93f27273
Author(s)
Shah, Darsh J.(Darsh Jaidip)
Advisor(s)
Regina Barzilay.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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