Cross-lingual Contextualized Topic Models with Zero-shot Learning
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.
Cite
@article{arxiv.2004.07737,
title = {Cross-lingual Contextualized Topic Models with Zero-shot Learning},
author = {Federico Bianchi and Silvia Terragni and Dirk Hovy and Debora Nozza and Elisabetta Fersini},
journal= {arXiv preprint arXiv:2004.07737},
year = {2021}
}
Comments
Updated version. Published as a conference paper at EACL2021