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

arXiv: https://arxiv.org/abs/2601.11872 · HTML: https://mlsanity.com/paper/2601.11872/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors](https://mlsanity.com/paper/2203.01825/) (2022)
- [How good are variational autoencoders at transfer learning?](https://mlsanity.com/paper/2304.10767/) (2023)
- [Refining Dimensions for Improving Clustering-based Cross-lingual Topic Models](https://mlsanity.com/paper/2412.12433/) (2024)

## Built on by (most cited first, 3 of 3)

- [MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining](https://mlsanity.com/paper/2604.24374/) (2026)
- [LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models](https://mlsanity.com/paper/2605.03299/) (2026)
- [TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching](https://mlsanity.com/paper/2605.12288/) (2026)

## Abstract (arXiv)

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.