Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM). Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method. This works as a regularization to properly align topics and prevent degenerate topic representations of words, which mitigates the repetitive topic issue. To address the low-coverage dictionary issue, we further propose a cross-lingual vocabulary linking method that finds more linked cross-lingual words for topic alignment beyond the translations of a given dictionary. Extensive experiments on English, Chinese, and Japanese datasets demonstrate that our method outperforms state-of-the-art baselines, producing more coherent, diverse, and well-aligned topics and showing better transferability for cross-lingual classification tasks.

翻译：跨语言主题模型通过揭示对齐的潜在主题，在跨语言文本分析中被广泛应用。然而，现有方法存在两个主要问题：生成重复主题阻碍后续分析，以及低覆盖率词典导致性能下降。本文提出基于互信息的跨语言主题建模方法（InfoCTM）。与之前工作采用直接对齐不同，我们提出基于互信息方法的主题对齐。该方法作为正则化项能实现主题的合理对齐，并防止词的主题表示退化，从而缓解重复主题问题。针对低覆盖率词典问题，我们进一步提出跨语言词汇链接方法，在给定词典翻译范围之外寻找更多可链接的跨语言词汇用于主题对齐。在英语、中文和日语数据集上的大量实验表明，本方法优于现有最先进的基线模型，能够生成更连贯、多样且对齐良好的主题，并在跨语言分类任务中展现出更优的迁移性能。