Cross-lingual Contextualized Topic Models with Zero-shot Learning

Debora Nozza; Dirk Hovy; Elisabetta Fersini; Federico Bianchi; Silvia Terragni

Cross-lingual Contextualized Topic Models with Zero-shot Learning

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arxiv 2004.07737 v2 pith:67DHYZFE submitted 2020-04-16 cs.CL

Cross-lingual Contextualized Topic Models with Zero-shot Learning

Federico Bianchi , Silvia Terragni , Dirk Hovy , Debora Nozza , Elisabetta Fersini This is my paper

classification cs.CL

keywords languagestopicmodelscross-lingualdifferentherelearningmodel

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Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in multiple languages. They all cover the same content, but the linguistic differences make it impossible to use traditional, bag-of-word-based topic models. Models have to be either single-language or suffer from a huge, but extremely sparse vocabulary. Both issues can be addressed by transfer learning. In this paper, we introduce a zero-shot cross-lingual topic model. Our model learns topics on one language (here, English), and predicts them for unseen documents in different languages (here, Italian, French, German, and Portuguese). We evaluate the quality of the topic predictions for the same document in different languages. Our results show that the transferred topics are coherent and stable across languages, which suggests exciting future research directions.

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