InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling

• URL: http://arxiv.org/abs/2304.03544v2
• Date: Wed, 27 Mar 2024 10:53:42 GMT
• Title: InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling
• Authors: Xiaobao Wu, Xinshuai Dong, Thong Nguyen, Chaoqun Liu, Liangming Pan, Anh Tuan Luu,
• Abstract summary: Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. Most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. We propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM) to produce more coherent, diverse, and well-aligned topics.
• Score: 40.54497836775837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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