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Funnelling ( Fun ) is a recently proposed method for cross-lingual text classification (CLTC) based on a two-tier learning ensemble for heterogeneous transfer learning (HTL). In this ensemble method, 1st-tier classifiers, each working on a different and language-dependent feature space, return a vector of calibrated posterior probabilities (with one dimension for each class) for each document, and the final classification decision is taken by a meta-classifier that uses this vector as its input. The meta-classifier can thus exploit class-class correlations, and this (among other things) gives Fun an edge over CLTC systems in which these correlations cannot be brought to bear. In this paper we describe Generalized Funnelling ( gFun ), a generalisation of Fun consisting of an HTL architecture in which 1st-tier components can be arbitrary view-generating functions , i.e., language-dependent functions that each produce a language-independent representation (“view”) of the (monolingual) document. We describe an instance of gFun in which the meta-classifier receives as input a vector of calibrated posterior probabilities (as in Fun ) aggregated to other embedded representations that embody other types of correlations, such as word-class correlations (as encoded by Word-Class Embeddings ), word-word correlations (as encoded by Multilingual Unsupervised or Supervised Embeddings ), and word-context correlations (as encoded by multilingual BERT ). We show that this instance of gFun substantially improves over Fun and over state-of-the-art baselines, by reporting experimental results obtained on two large, standard datasets for multilingual multilabel text classification. Our code that implements gFun is publicly available.

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