# Neural Structural Correspondence Learning for Domain Adaptation

@article{Ziser2017NeuralSC,
title={Neural Structural Correspondence Learning for Domain Adaptation},
author={Yftah Ziser and Roi Reichart},
journal={ArXiv},
year={2017},
volume={abs/1610.01588}
}
• Published 5 October 2016
• Computer Science
• ArXiv
Domain adaptation, adapting models from domains rich in labeled training data to domains poor in such data, is a fundamental NLP challenge. We introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural networks. Particularly, our model is a three-layer neural network that learns to encode the nonpivot features of…
89 Citations

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