Fast on-line learning for multilingual categorization

TL;DR: This work introduces an on-line learning scheme, where language-specific updates are interleaved in order to iteratively optimize the global cost in one pass, and shows that this produces similar performance as the batch approach, at a fraction of the computational cost.

Abstract: Multiview learning has been shown to be a natural and efficient framework for supervised or semi-supervised learning of multilingual document categorizers. The state-of-the-art co-regularization approach relies on alternate minimizations of a combination of language-specific categorization errors and a disagreement between the outputs of the monolingual text categorizers. This is typically solved by repeatedly training categorizers on each language with the appropriate regularizer. We extend and improve this approach by introducing an on-line learning scheme, where language-specific updates are interleaved in order to iteratively optimize the global cost in one pass. Our experimental results show that this produces similar performance as the batch approach, at a fraction of the computational cost.