  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.
