1,572 research outputs found
Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning
In many multilingual text classification problems, the documents in different
languages often share the same set of categories. To reduce the labeling cost
of training a classification model for each individual language, it is
important to transfer the label knowledge gained from one language to another
language by conducting cross language classification. In this paper we develop
a novel subspace co-regularized multi-view learning method for cross language
text classification. This method is built on parallel corpora produced by
machine translation. It jointly minimizes the training error of each classifier
in each language while penalizing the distance between the subspace
representations of parallel documents. Our empirical study on a large set of
cross language text classification tasks shows the proposed method consistently
outperforms a number of inductive methods, domain adaptation methods, and
multi-view learning methods.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
Deep learning techniques have been successfully used in learning a common
representation for multi-view data, wherein the different modalities are
projected onto a common subspace. In a broader perspective, the techniques used
to investigate common representation learning falls under the categories of
canonical correlation-based approaches and autoencoder based approaches. In
this paper, we investigate the performance of deep autoencoder based methods on
multi-view data. We propose a novel step-based correlation multi-modal CNN
(CorrMCNN) which reconstructs one view of the data given the other while
increasing the interaction between the representations at each hidden layer or
every intermediate step. Finally, we evaluate the performance of the proposed
model on two benchmark datasets - MNIST and XRMB. Through extensive
experiments, we find that the proposed model achieves better performance than
the current state-of-the-art techniques on joint common representation learning
and transfer learning tasks.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017
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