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2015

A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features

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A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features
We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently outperforms a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.
Jianfei Yu, Jing Jiang
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Jianfei Yu, Jing Jiang
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