Papers by Felipe Gonzalez-Pizarro
Diversity-Aware Coherence Loss for Improving Neural Topic Models (2023.acl-short)
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| Challenge: | Experimental results show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters. |
| Approach: | They propose a variational autoencoder framework that minimizes the posterior and prior divergence and a diversity-aware coherence loss that encourages the model to learn corpus-level coherency scores while maintaining high diversity between topics. |
| Outcome: | The proposed approach significantly improves the performance of neural topic models without pretraining or additional parameters. |
Neural Multimodal Topic Modeling: A Comprehensive Evaluation (2024.lrec-main)
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| Challenge: | Neural topic models can find coherent and diverse topics in textual data, but they are limited in dealing with multimodal datasets. |
| Approach: | They propose two new topic modeling solutions and two new evaluation metrics for document multimodality. |
| Outcome: | The proposed models generate coherent and diverse topics on a rich dataset. |