Cross-Lingual Adaptation Using Structural Correspondence Learning
نویسندگان
چکیده
منابع مشابه
Neural Structural Correspondence Learning for Domain Adaptation
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 ...
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Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resourcerich source domain to a resource-poor target ...
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ژورنال
عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology
سال: 2011
ISSN: 2157-6904,2157-6912
DOI: 10.1145/2036264.2036277