In this paper, we propose to tackle cross language text classification by inducing cross-lingual predictive data representations with both labeled and unlabeled documents from the two language domains.
we propose a cross-lingual log-bilinear document model to learn distributed representations of words, which can capture both the semantic sim-ilarities of words across languages and the predictive information with respect to the target classification task.