Knowledge Transfer for Utterance Classification in Low-Resource Languages

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9811)

Abstract

The paper deals with a problem of short text classification in Kazakh. Traditional text classification approaches require labeled data to build accurate classifiers. However the amount of available labeled data is usually very limited due to high cost of labeling or data accessibility issues. We describe a method of constructing a classifier without labeled data in the target language. A convolutional neural network (CNN) is trained on Russian labeled texts and a language vector space transform is used to transfer knowledge from Russian into Kazakh. Classification accuracy is evaluated on a dataset of customer support requests. The presented method demonstrates competitive results compared with an approach that employed a sophisticated automatic translation system.

Keywords

Text classification Language vector space Word embeddings CNN Low-resource 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.STC-InnovationsSaint PetersburgRussia
  2. 2.Speech Technology CenterSaint PetersburgRussia
  3. 3.ITMO UniversitySaint PetersburgRussia

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