The invention provides cross-language unsupervised classification with multi-view transfer learning. Embodiments of an unsupervised cross-language sentiment classification model, which may be referred to as a multi-view encoder classifier (MVEC), utilizing an unsupervised machine translation (UMT) system and a language discriminator are presented. Unlike previous Language Model (LM)-based fine tuning methods that adjust parameters based only on classification errors of training data, embodiments employ an encoder-decoder framework of UMT as a regularization component on shared network parameters. In one or more embodiments, a cross-language encoder of an embodiment learns a shared representation, which is effective for reconstructing input sentences of both languages and generating more representative views from the input for classification. Experiments on five language pairs prove that the embodiment of the MVEC is obviously superior to other models for 8/11 sentiment classification tasks.
Cross-language unsupervised classification with multi-view transfer learning