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.

本文介绍了一种零射击跨语言主题模型，利用迁移学习来处理多个语言的数据集，以解决传统基于词袋的主题模型所面临的单语言或巨大而稀疏的词汇表等问题，并评估了在不同语言中同一篇文章的主题预测的准确性和连贯性，结果表明所转移的主题是连贯且稳定的，具有潜在的未来研究方向。