Heterogeneous document embeddings for cross-lingual text classification
Alejandro Moreo, Andrea Pedrotti, Fabrizio Sebastiani · 2021
Funnelling (Fun) is a method for cross-lingual text classification (CLC) based on a two-tier ensemble for heterogeneous transfer learning. In Fun, 1st-tier classifiers, each working on a different, 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 metaclassifier can thus exploit class-class correlations, and this (among other things) gives Fun an edge over CLC systems where these correlations cannot be leveraged.