Cross-lingual Contextualized Topic Models with Zero-shot Learning

Our Model, ZeroShotTM

Abstract

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.

Publication
In Proceedings of The 16th Conference of the European Chapter of the Association for Computational Linguistics
Federico Bianchi
Federico Bianchi
Postdoctoral Researcher

My research interests include meaning in natural language and programming languages.

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