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• W1506572002 abstract "We use L1 regularized transductive regression to learn mappings between source and target features of the training sets derived for each test sentence and use these mappings to rerank translation outputs. We compare the effectiveness of L1 regularization techniques for regression to learn mappings between features given in a sparse feature matrix. The results show the effectiveness of using L1 regularization versus L2 used in ridge regression. We show that regression mapping is effective in reranking translation outputs and in selecting the best system combinations with encouraging results on different language pairs." @default.
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• W1506572002 date "2010-07-15" @default.
• W1506572002 modified "2023-09-23" @default.
• W1506572002 title "L1 Regularized Regression for Reranking and System Combination in Machine Translation" @default.
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