GloCTM: Cross-Lingual Topic Modeling via a Global Context Space

• URL: http://arxiv.org/abs/2601.11872v1
• Date: Sat, 17 Jan 2026 01:45:31 GMT
• Title: GloCTM: Cross-Lingual Topic Modeling via a Global Context Space
• Authors: Nguyen Tien Phat, Ngo Vu Minh, Linh Van Ngo, Nguyen Thi Ngoc Diep, Thien Huu Nguyen,
• Abstract summary: GloCTM is a novel framework that enforces cross-lingual topic alignment through a unified semantic space spanning the entire model pipeline.<n>At the output level, the global topic-word distribution, defined over the combined vocabulary, structurally synchronizes topic meanings across languages.
• Score: 28.89996742581612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-lingual topic modeling seeks to uncover coherent and semantically aligned topics across languages - a task central to multilingual understanding. Yet most existing models learn topics in disjoint, language-specific spaces and rely on alignment mechanisms (e.g., bilingual dictionaries) that often fail to capture deep cross-lingual semantics, resulting in loosely connected topic spaces. Moreover, these approaches often overlook the rich semantic signals embedded in multilingual pretrained representations, further limiting their ability to capture fine-grained alignment. We introduce GloCTM (Global Context Space for Cross-Lingual Topic Model), a novel framework that enforces cross-lingual topic alignment through a unified semantic space spanning the entire model pipeline. GloCTM constructs enriched input representations by expanding bag-of-words with cross-lingual lexical neighborhoods, and infers topic proportions using both local and global encoders, with their latent representations aligned through internal regularization. At the output level, the global topic-word distribution, defined over the combined vocabulary, structurally synchronizes topic meanings across languages. To further ground topics in deep semantic space, GloCTM incorporates a Centered Kernel Alignment (CKA) loss that aligns the latent topic space with multilingual contextual embeddings. Experiments across multiple benchmarks demonstrate that GloCTM significantly improves topic coherence and cross-lingual alignment, outperforming strong baselines.

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