# Learning Multilingual Topics from Incomparable Corpus

arXiv: https://arxiv.org/abs/1806.04270 · HTML: https://mlsanity.com/paper/1806.04270/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling](https://mlsanity.com/paper/2304.03544/) (2023)
- [Towards the TopMost: A Topic Modeling System Toolkit](https://mlsanity.com/paper/2309.06908/) (2023)
- [Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling](https://mlsanity.com/paper/2104.05064/) (2021)
- [Multilingual and Multimodal Topic Modelling with Pretrained Embeddings](https://mlsanity.com/paper/2211.08057/) (2022)
- [Embedded Topic Models Enhanced by Wikification](https://mlsanity.com/paper/2410.02441/) (2024)

## Abstract (arXiv)

Multilingual topic models enable crosslingual tasks by extracting consistent topics from multilingual corpora. Most models require parallel or comparable training corpora, which limits their ability to generalize. In this paper, we first demystify the knowledge transfer mechanism behind multilingual topic models by defining an alternative but equivalent formulation. Based on this analysis, we then relax the assumption of training data required by most existing models, creating a model that only requires a dictionary for training. Experiments show that our new method effectively learns coherent multilingual topics from partially and fully incomparable corpora with limited amounts of dictionary resources.