L1 Regularized Regression for Reranking and System Combination in Machine Translation

by Ergun Biçici , Deniz Yuret

Datum	Value	Source
TITLE	L1 Regularized Regression for Reranking and System Combination in Machine Translation	SVM HeaderParse 0.2
AUTHOR NAME	Ergun Biçici	SVM HeaderParse 0.2
AUTHOR AFFIL	Koç University	SVM HeaderParse 0.2
AUTHOR ADDR	34450 Sariyer, Istanbul, Turkey	SVM HeaderParse 0.2
AUTHOR NAME	Deniz Yuret	SVM HeaderParse 0.2
AUTHOR AFFIL	Koç University	SVM HeaderParse 0.2
AUTHOR ADDR	34450 Sariyer, Istanbul, Turkey	SVM HeaderParse 0.2
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. 1	SVM HeaderParse 0.2
CITATIONS	18 found	ParsCit 1.0