We use transductive regression techniques to learn mappings between source
and target features of given parallel corpora and use these mappings to
generate machine translation outputs. We show the effectiveness of $L_1$
regularized regression (\textit{lasso}) to learn the mappings between sparsely
observed feature sets versus $L_2$ regularized regression. Proper selection of
training instances plays an important role to learn correct feature mappings
within limited computational resources and at expected accuracy levels. We
introduce \textit{dice} instance selection method for proper selection of
training instances, which plays an important role to learn correct feature
mappings for improving the source and target coverage of the training set. We
show that $L_1$ regularized regression performs better than $L_2$ regularized
regression both in regression measurements and in the translation experiments
using graph decoding. We present encouraging results when translating from
German to English and Spanish to English. We also demonstrate results when the
phrase table of a phrase-based decoder is replaced with the mappings we find
with the regression model.

使用跨模型回归技术学习平行语料库的源和目标特征之间的映射，并将这些映射用于生成机器翻译输出，在选择正确的训练实例方面，我们引入了一种名为 dice 的实例选择方法，该方法在限定的计算资源和预期的准确性水平上起到了重要的作用。通过使用 $L_1$ 正则化回归，我们展示了在回归测量和使用图解码进行翻译实验时，其性能优于 $L_2$ 正则化回归。我们在从德语到英语和西班牙语到英语的翻译中呈现了令人鼓舞的结果，并演示了用回归模型发现的映射替换短语解码器的短语表的结果。