@inproceedings{guo-etal-2018-multi,
title = "Multi-Source Domain Adaptation with Mixture of Experts",
author = "Guo, Jiang and
Shah, Darsh and
Barzilay, Regina",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1498",
doi = "10.18653/v1/D18-1498",
pages = "4694--4703",
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.",
}
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%0 Conference Proceedings
%T Multi-Source Domain Adaptation with Mixture of Experts
%A Guo, Jiang
%A Shah, Darsh
%A Barzilay, Regina
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F guo-etal-2018-multi
%X 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.
%R 10.18653/v1/D18-1498
%U https://aclanthology.org/D18-1498
%U https://doi.org/10.18653/v1/D18-1498
%P 4694-4703
Markdown (Informal)
[Multi-Source Domain Adaptation with Mixture of Experts](https://aclanthology.org/D18-1498) (Guo et al., EMNLP 2018)
ACL
- Jiang Guo, Darsh Shah, and Regina Barzilay. 2018. Multi-Source Domain Adaptation with Mixture of Experts. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4694–4703, Brussels, Belgium. Association for Computational Linguistics.