Felipe Gonzalez-Pizarro — accepted AI-conference papers

2024

Neural Multimodal Topic Modeling: A Comprehensive Evaluation

Felipe Gonzalez-Pizarro, Giuseppe Carenini

COLING 2024main

Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This paper presents the first systematic and comprehensive evaluation of multimodal topic modeling of documents containing bot…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2023

Diversity-Aware Coherence Loss for Improving Neural Topic Models

Raymond Li, Felipe Gonzalez-Pizarro, Linzi Xing, Gabriel Murray, Giuseppe Carenini

ACL 2023short

The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. Since neural topic models are trained by recreating individual input documents, t…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗