mlsanityData to 7 Oct 2026

Multilingual and Multimodal Topic Modelling with Pretrained Embeddings

arXiv 2211.08057 ↗First posted 15 Nov 2022cs.CL cs.AIcited by 6

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Abstract (arXiv)

This paper presents M3L-Contrast -- a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space. Our model is trained jointly on texts and images and takes advantage of pretrained document and image embeddings to abstract the complexities between different languages and modalities. As a multilingual topic model, it produces aligned language-specific topics and as multimodal model, it infers textual representations of semantic concepts in images. We demonstrate that our model is competitive with a zero-shot topic model in predicting topic distributions for comparable multilingual data and significantly outperforms a zero-shot model in predicting topic distributions for comparable texts and images. We also show that our model performs almost as well on unaligned embeddings as it does on aligned embeddings.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 42 references that are in the map. First posted on arXiv 15 Nov 2022.

Builds on

22 of 42 references.

References · 22

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10 more
  1. 2017Autoencoding Variational Inference For Topic Modelscited by 168
  2. 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
  3. 2020Cross-lingual Contextualized Topic Models with Zero-shot Learningcited by 66
  4. 2021Contrastive Learning for Neural Topic Modelcited by 48
  5. 2020Improving Neural Topic Models using Knowledge Distillationcited by 25
  6. 2018ATM:Adversarial-neural Topic Modelcited by 23
  7. 2021Multilingual Molecular Representation Learning via Contrastive Pre-trainingcited by 12
  8. 2021Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modelingcited by 8
  9. 2021Evaluation of Audio-Visual Alignments in Visually Grounded Speech Modelscited by 8
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 1.

6 papers.

  1. 2023Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modelingcited by 16
  2. 2024Neural Multimodal Topic Modeling: A Comprehensive Evaluationcited by 5
  3. 2025CEMTM: Contextual Embedding-based Multimodal Topic Modelingcited by 4
  4. 2025An Image is Worth K Topics: A Visual Structural Topic Model with Pretrained Image Embeddingscited by 1
  5. 2025Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Mediacited by 1
  6. 2026Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Datacited by 0