Summary of Probabilistic Topic Modelling with Transformer Representations, by Arik Reuter et al.
Probabilistic Topic Modelling with Transformer Representations
by Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken, Thomas Kneib
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes the Transformer-Representation Neural Topic Model (TNTM), a novel approach that combines the benefits of topic representations in transformer-based embedding spaces and probabilistic modelling. By leveraging the variational autoencoder (VAE) framework, TNTM achieves improved inference speed and modelling flexibility while maintaining perfect topic diversity. The model is compared to state-of-the-art approaches, showing results on par with them in terms of embedding coherence. This paper unifies the powerful notion of topics based on transformer embeddings with fully probabilistic modelling, as seen in models like Latent Dirichlet Allocation (LDA). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand topics by combining two ideas: using special transformations to represent words and using math to model these representations. It’s called the Transformer-Representation Neural Topic Model, or TNTM for short. This approach is good because it’s fast and flexible, making it useful for big datasets. The results show that this method works just as well as other state-of-the-art methods in understanding word meanings. |
Keywords
* Artificial intelligence * Embedding * Inference * Transformer * Variational autoencoder
