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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 (NNs). Our model is a three-layer NN that learns to encode the non-pivot features of an input example into a lowdimensional representation, so that the existence of pivot features (features that are prominent in both domains and convey usefuldoi:10.18653/v1/k17-1040 dblp:conf/conll/ZiserR17 fatcat:hvkpr7vulvhibcjbshc5toeqny