Geert Heyman

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We study the problem of extracting cross-lingual topics from non-parallel multilingual text datasets with partially overlapping thematic content (e.g., aligned Wikipedia articles in two different languages). To this end, we develop a new bilingual probabilistic topic model called comparable bilingual latent Dirichlet allocation (C-BiLDA), which is able to(More)
We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term(More)
MTTT is an open-source tool conceived to help students and non-savvy users get started with the core technologies involved in a classical workflow of MT+PE without having to deal with the purely technical aspects of installing, training and evaluating MT models. In that sense, this tool is a graphical user interface abstracting the underlying command; it(More)
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