Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification

Cross-lingual Text Classification(CLC) consists of automatically classifying, according to a common setCofclasses, documents each written in one of a set of languagesL, and doing so more accurately than when"naïvely" classifying each document via its corresponding language-specific classifier. In order to obtain anincrease in the classification accuracy for a given language, the system thus needs to also leverage the trainingexamples written in the other languages. We tackle "multilabel" CLC viafunnelling, a new ensemble learningmethod that we propose here. Funnelling consists of generating a two-tier classification system where alldocuments, irrespectively of language, are classified by the same (2nd-tier) classifier. For this classifier alldocuments are represented in a common, language-independent feature space consisting of the posteriorprobabilities generated by 1st-tier, language-dependent classifiers. This allows the classification of all testdocuments, of any language, to benefit from the information present in all training documents, of any language.We present substantial experiments, run on publicly available multilingual text collections, in which funnellingis shown to significantly outperform a number of state-of-the-art baselines. All code and datasets (in vectorform) are made publicly available.