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

TL;DR

GloCTM introduces a unified semantic framework for cross-lingual topic modeling, leveraging multilingual embeddings and enriched representations to improve coherence and alignment across languages.

Contribution

It proposes a novel global context space that aligns topics across languages using enriched inputs, dual encoders, and a CKA loss, advancing beyond previous disjoint models.

Findings

Significantly improves topic coherence across languages.

Achieves better cross-lingual alignment than baseline models.

Demonstrates effectiveness on multiple benchmark datasets.

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.